modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 18:26:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 558
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-13 18:25:20
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755636637
|
vwzyrraz7l
| 2025-08-19T21:18:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:18:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/66376
|
ultratopaz
| 2025-08-19T21:18:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:18:46Z |
[View on Civ Archive](https://civarchive.com/models/23128?modelVersionId=95699)
|
seraphimzzzz/28092
|
seraphimzzzz
| 2025-08-19T21:18:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:18:38Z |
[View on Civ Archive](https://civarchive.com/models/23128?modelVersionId=34142)
|
crystalline7/108814
|
crystalline7
| 2025-08-19T21:18:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:18:19Z |
[View on Civ Archive](https://civarchive.com/models/133033?modelVersionId=146389)
|
ultratopaz/22481
|
ultratopaz
| 2025-08-19T21:18:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:18:06Z |
[View on Civ Archive](https://civarchive.com/models/21745?modelVersionId=27137)
|
ultratopaz/74089
|
ultratopaz
| 2025-08-19T21:17:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:17:22Z |
[View on Civ Archive](https://civarchive.com/models/98563?modelVersionId=105415)
|
ultratopaz/96648
|
ultratopaz
| 2025-08-19T21:17:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:17:12Z |
[View on Civ Archive](https://civarchive.com/models/40666?modelVersionId=86556)
|
crystalline7/87818
|
crystalline7
| 2025-08-19T21:16:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:16:52Z |
[View on Civ Archive](https://civarchive.com/models/113018?modelVersionId=122063)
|
crystalline7/57410
|
crystalline7
| 2025-08-19T21:16:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:16:35Z |
[View on Civ Archive](https://civarchive.com/models/79193?modelVersionId=83990)
|
Muapi/flux-graphic-t-shirt-designs
|
Muapi
| 2025-08-19T21:15:05Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:14:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux Graphic T-Shirt Designs
**Base model**: Flux.1 D
**Trained words**: T-Shirt Art, Graphic T-Shirt, Vector Art, T-Shirt Graphic
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:721090@819874", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/yfg-aarchy-flux
|
Muapi
| 2025-08-19T21:14:48Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:14:25Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# YFG Aarchy [Flux]
**Base model**: Flux.1 D
**Trained words**: YFG-Aarchy
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1108935@1245947", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755638056
|
Dejiat
| 2025-08-19T21:14:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:14:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/33435
|
seraphimzzzz
| 2025-08-19T21:13:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:13:21Z |
[View on Civ Archive](https://civarchive.com/models/38290?modelVersionId=44242)
|
Muapi/topmodel
|
Muapi
| 2025-08-19T21:13:25Z | 0 | 0 | null |
[
"safetensors",
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:13:18Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# topmodel
**Base model**: Flux.1 D
**Trained words**: A stunning woman with a sculpted body, perfectly proportioned curves, and a flawless face. Her skin is radiant, and her facial features are symmetrical and harmonious, highlighting large expressive eyes, full lips, and a captivating smile. She is wearing a casual outfit consisting of a fitted T-shirt and jeans that accentuate her figure. In another scene, she is dressed in elegant lingerie, including a delicate bra and matching panties, showcasing her perfect physique, A breathtaking woman, combining an athletic and well-defined body with a face of classic beauty. Her eyes are piercing, and her hair falls softly around her face, framing her delicate features. She is wearing a sophisticated dress that hugs her curves, enhancing her magnetic presence. In another setting, she is seen in a comfortable casual outfit with a stylish blouse and skirt, and later in luxurious lingerie, featuring a lacy bra and matching underwear, A woman with an impressive physique and an absolutely perfect face, worthy of a work of art. Her skin is smooth and flawless, her eyes shine with a captivating intensity, and her lips are perfectly shaped. She is in a luxurious setting, wearing a stunning evening dress that highlights every detail of her mesmerizing figure. In another scene, she is casually dressed in a fitted T-shirt and shorts, and also appears in intimate lingerie, including a silk bra and matching panties
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:708602@792571", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/63792
|
crystalline7
| 2025-08-19T21:12:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-05T01:12:37Z |
[View on Civ Archive](https://civarchive.com/models/85323?modelVersionId=91647)
|
Muapi/wolfie-s-3d-topology-flux-character-concept
|
Muapi
| 2025-08-19T21:12:19Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:12:06Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Wolfie's 3D Topology FLUX (Character Concept)
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:854215@955699", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/137033
|
ultratopaz
| 2025-08-19T21:12:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:12:06Z |
[View on Civ Archive](https://civarchive.com/models/159259?modelVersionId=179078)
|
Muapi/adepta-sororitas-sisters-of-battle-warhammer-40k-flux
|
Muapi
| 2025-08-19T21:11:57Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:11:41Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Adepta Sororitas (Sisters of Battle) Warhammer 40K | Flux
**Base model**: Flux.1 D
**Trained words**: SisB40K
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1119858@1258572", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/40798
|
seraphimzzzz
| 2025-08-19T21:11:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:11:46Z |
[View on Civ Archive](https://civarchive.com/models/52773?modelVersionId=57170)
|
ultratopaz/86831
|
ultratopaz
| 2025-08-19T21:11:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:11:33Z |
[View on Civ Archive](https://civarchive.com/models/111982?modelVersionId=120872)
|
NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes
|
NYUAD-ComNets
| 2025-08-19T21:11:23Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"arxiv:2412.14197",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-06-29T19:19:59Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Llama3.2-11B based Hate Detection in Arabic MultiModal Memes
The rise of social media and online communication platforms has led to the spread of Arabic memes as a key form of digital expression.
While these contents can be humorous and informative, they are also increasingly being used to spread offensive language and hate speech.
Consequently, there is a growing demand for precise analysis of content in Arabic meme.
This work used Llama 3.2 with its vision capability to effectively identify hate content within Arabic memes.
The evaluation is conducted using a dataset of Arabic memes proposed in the ArabicNLP MAHED 2025 challenge.
The results underscore the capacity of ***Llama 3.2-11B fine-tuned with Arabic memes***, to deliver the superior performance.
They achieve **accuracy** of **80.3%** and **macro F1 score** of **73.3%**.
The proposed solutions offer a more nuanced understanding of memes for accurate and efficient Arabic content moderation systems.
# Examples of Arabic Memes from ArabicNLP MAHED 2025 challenge
# Examples
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jBuVCt5163WlugFRXkSgq.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jiPId6f5IiGXxpI898llC.jpeg"> |
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/61acyltUsTB--ZOAMkv0a.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/_alSRnwG0azE_iYq2BrpP.jpeg"> |
``` python
import pandas as pd
import os
from unsloth import FastVisionModel
import torch
from datasets import load_dataset
from transformers import TextStreamer
from PIL import Image
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
model_name = "NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes"
model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxxxxxxxxxxxxxxx')
FastVisionModel.for_inference(model)
dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test")
print(dataset_test)
def add_labels_column(example):
example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate"
return example
dataset_test = dataset_test.map(add_labels_column)
pred=[]
for k in range(606):
image = dataset_test[k]["image"]
text = dataset_test[k]["text"]
lab = dataset_test[k]["labels"]
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": text}
]}
]
input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True)
inputs = tokenizer(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = False, temperature = 0.3, min_p = 0.3)
p = tokenizer.decode(p[0], skip_special_tokens=True)
pred.append(p.split('assistant')[1].strip())
print(pred)
```
We used Low-Rank Adaptation (LoRA) as the Parameter-Efficient Fine-Tuning (PEFT) method for fine-tuning utilizing the unsloth framework.
The hyper-parameters of Llama 3.2-11B are as follows:
the training batch size per device is set to 4.
gradients are accumulated over 4 steps.
the learning rate warm-up lasts for 5 steps.
the total number of training steps is 150.
the learning rate is set to 0.0002.
the optimizer used is 8-bit AdamW
weight decay is set to 0.01.
a linear learning rate scheduler is used.
# BibTeX entry and citation info
```
@misc{aldahoul2024advancingvehicleplaterecognition,
title={Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models},
author={Nouar AlDahoul and Yasir Zaki},
year={2025},
eprint={2412.14197},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.14197},
}
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755637845
|
lilTAT
| 2025-08-19T21:11:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:11:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/mnemonic-style
|
Muapi
| 2025-08-19T21:11:13Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:11:01Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Mnemonic Style
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:106263@776600", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/150594
|
ultratopaz
| 2025-08-19T21:10:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:10:00Z |
[View on Civ Archive](https://civarchive.com/models/175296?modelVersionId=196819)
|
Muapi/gpt-image-1-style-flux
|
Muapi
| 2025-08-19T21:10:02Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:09:54Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# GPT Image 1 Style [FLUX]
**Base model**: Flux.1 D
**Trained words**: aidmagptimage
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1554812@1759376", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/37706
|
crystalline7
| 2025-08-19T21:09:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:09:37Z |
[View on Civ Archive](https://civarchive.com/models/47112?modelVersionId=51697)
|
Muapi/macaronflux-fashion-culture-magazine-pose-aesthetic
|
Muapi
| 2025-08-19T21:09:32Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:09:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# MacaronFLUX - fashion/culture magazine pose + aesthetic
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:999951@1120638", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/1029554
|
ultratopaz
| 2025-08-19T21:09:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:09:07Z |
[View on Civ Archive](https://civarchive.com/models/1002068?modelVersionId=1123110)
|
crystalline7/83662
|
crystalline7
| 2025-08-19T21:09:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:09:01Z |
[View on Civ Archive](https://civarchive.com/models/108726?modelVersionId=117096)
|
seraphimzzzz/33568
|
seraphimzzzz
| 2025-08-19T21:08:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:08:00Z |
[View on Civ Archive](https://civarchive.com/models/38628?modelVersionId=44548)
|
Muapi/richard-anderson
|
Muapi
| 2025-08-19T21:07:49Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:07:35Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Richard Anderson
**Base model**: Flux.1 D
**Trained words**: Art by Richard Anderson
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1349128@1523853", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/v-mix-style-lora-pony-illustrious-flux
|
Muapi
| 2025-08-19T21:07:08Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:06:58Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# V-mix - style LORA [PONY + Illustrious + FLUX]
**Base model**: Flux.1 D
**Trained words**: A illustration in the vmix style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:976618@1226695", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/57878
|
seraphimzzzz
| 2025-08-19T21:06:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:06:37Z |
[View on Civ Archive](https://civarchive.com/models/79935?modelVersionId=84759)
|
ultratopaz/64194
|
ultratopaz
| 2025-08-19T21:06:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:06:17Z |
[View on Civ Archive](https://civarchive.com/models/87387?modelVersionId=92998)
|
ultratopaz/1031331
|
ultratopaz
| 2025-08-19T21:04:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:04:35Z |
[View on Civ Archive](https://civarchive.com/models/124035?modelVersionId=1126617)
|
matboz/ring-gemma-3
|
matboz
| 2025-08-19T21:04:28Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-3-27b-it",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-3-27b-it",
"region:us"
] |
text-generation
| 2025-08-19T21:04:07Z |
---
base_model: google/gemma-3-27b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-3-27b-it
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
crystalline7/20731
|
crystalline7
| 2025-08-19T21:04:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:04:14Z |
[View on Civ Archive](https://civarchive.com/models/21003?modelVersionId=24998)
|
crystalline7/75403
|
crystalline7
| 2025-08-19T21:03:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:03:39Z |
[View on Civ Archive](https://civarchive.com/models/71861?modelVersionId=107072)
|
Muapi/dark-landscapes
|
Muapi
| 2025-08-19T21:03:14Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:02:52Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Dark Landscapes
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:702293@785760", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/14708
|
crystalline7
| 2025-08-19T21:02:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:02:36Z |
[View on Civ Archive](https://civarchive.com/models/14119?modelVersionId=17526)
|
seraphimzzzz/19648
|
seraphimzzzz
| 2025-08-19T21:02:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:01:58Z |
[View on Civ Archive](https://civarchive.com/models/19939?modelVersionId=23677)
|
zhuojing-huang/gpt2-arabic-english-ewc-2
|
zhuojing-huang
| 2025-08-19T21:02:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:39:42Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt2-arabic-english-ewc-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. -->
# gpt2-arabic-english-ewc-2
This model was trained from scratch 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: 0.0005
- train_batch_size: 64
- 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
- lr_scheduler_warmup_steps: 30
- training_steps: 61035
### Training results
### Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755637270
|
Dejiat
| 2025-08-19T21:01:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:01:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/15243
|
crystalline7
| 2025-08-19T21:00:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:00:44Z |
[View on Civ Archive](https://civarchive.com/models/15449?modelVersionId=18218)
|
ultratopaz/47366
|
ultratopaz
| 2025-08-19T20:58:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:58:55Z |
[View on Civ Archive](https://civarchive.com/models/63178?modelVersionId=67716)
|
ver-videos-intimo-de-abigail-lalama/ver.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral.en.redes.sociales
|
ver-videos-intimo-de-abigail-lalama
| 2025-08-19T20:58:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:58:20Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
|
New-original-archita-phukan-viral-video-on/New.full.videos.archita.Phukan.Viral.Video.Official.Tutorial
|
New-original-archita-phukan-viral-video-on
| 2025-08-19T20:55:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:55:08Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
Muapi/ars-midjourney-style-flux
|
Muapi
| 2025-08-19T20:52:49Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:52:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Ars MidJourney Style - Flux
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:650086@727320", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/flux.1-dev-cctv-mania
|
Muapi
| 2025-08-19T20:51:46Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:51:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FLUX.1 DEV - CCTV Mania
**Base model**: Flux.1 D
**Trained words**: CCTV Footage
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:684810@766464", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755636537
|
Dombili2038
| 2025-08-19T20:49:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:49:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
islamytchaev/autotrain-h21ao-zct4v
|
islamytchaev
| 2025-08-19T20:48:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T17:20:35Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1755634850
|
koloni
| 2025-08-19T20:47:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:47:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755636372
|
Dejiat
| 2025-08-19T20:47:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:46:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755636230
|
Dombili2038
| 2025-08-19T20:44:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:44:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/ege-8b-1.1-GGUF
|
mradermacher
| 2025-08-19T20:43:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"sft",
"unsloth",
"tr",
"dataset:orkungedik/function_call",
"base_model:orkungedik/ege-8b-1.1",
"base_model:quantized:orkungedik/ege-8b-1.1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:03:51Z |
---
base_model: orkungedik/ege-8b-1.1
datasets:
- orkungedik/function_call
language:
- tr
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- sft
- unsloth
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/orkungedik/ege-8b-1.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnonymousCS/xlmr_immigration_combo2_0
|
AnonymousCS
| 2025-08-19T20:40:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T20:14:43Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo2_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo2_0
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1953
- Accuracy: 0.9396
- 1-f1: 0.9051
- 1-recall: 0.8649
- 1-precision: 0.9492
- Balanced Acc: 0.9209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1825 | 1.0 | 25 | 0.1735 | 0.9422 | 0.9105 | 0.8842 | 0.9385 | 0.9276 |
| 0.1833 | 2.0 | 50 | 0.1800 | 0.9370 | 0.9026 | 0.8764 | 0.9303 | 0.9218 |
| 0.1652 | 3.0 | 75 | 0.1953 | 0.9396 | 0.9051 | 0.8649 | 0.9492 | 0.9209 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755635920
|
Dombili2038
| 2025-08-19T20:39:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:39:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/xlmr_immigration_combo1_4
|
AnonymousCS
| 2025-08-19T20:36:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T20:33:20Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo1_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo1_4
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1608
- Accuracy: 0.9499
- 1-f1: 0.9246
- 1-recall: 0.9228
- 1-precision: 0.9264
- Balanced Acc: 0.9431
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1938 | 1.0 | 25 | 0.1423 | 0.9576 | 0.9364 | 0.9382 | 0.9346 | 0.9527 |
| 0.1683 | 2.0 | 50 | 0.1662 | 0.9486 | 0.9237 | 0.9344 | 0.9132 | 0.9450 |
| 0.1651 | 3.0 | 75 | 0.1608 | 0.9499 | 0.9246 | 0.9228 | 0.9264 | 0.9431 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
noebreton/qwen2-7b-instruct-trl-sft-ChartQA
|
noebreton
| 2025-08-19T20:35:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:nvidia/Cosmos-Reason1-7B",
"base_model:finetune:nvidia/Cosmos-Reason1-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T15:46:03Z |
---
base_model: nvidia/Cosmos-Reason1-7B
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [nvidia/Cosmos-Reason1-7B](https://huggingface.co/nvidia/Cosmos-Reason1-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="noebreton/qwen2-7b-instruct-trl-sft-ChartQA", 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/noe-breton-aalto-university/cosmos-trl/runs/23tvci1p)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.56.0.dev0
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
SEDVW3/Full.18.Video.Debut.Angel.Avid.y.Milica.quien.me.siga.se.lo.paso
|
SEDVW3
| 2025-08-19T20:30:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:26:55Z |
<a href="https://allyoutubers.com/Video-Debut-Angel-Avid-y-Milica-quien-me-siga-se-lo-paso"> 🌐 Full.18.Video.Debut.Angel.Avid.y.Milica.quien.me.siga.se.lo.paso
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Video-Debut-Angel-Avid-y-Milica-quien-me-siga-se-lo-paso"> 🌐 Full.18.Video.Debut.Angel.Avid.y.Milica.quien.me.siga.se.lo.paso
<a href="https://allyoutubers.com/Video-Debut-Angel-Avid-y-Milica-quien-me-siga-se-lo-paso"> 🌐 Full.18.Video.Debut.Angel.Avid.y.Milica.quien.me.siga.se.lo.paso
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Video-Debut-Angel-Avid-y-Milica-quien-me-siga-se-lo-paso"> 🌐 Full.18.Video.Debut.Angel.Avid.y.Milica.quien.me.siga.se.lo.paso
|
New-Clip-prabh-viral-videos-hq/Original.New.full.videos.prabh.Viral.Video.Official.Tutorial
|
New-Clip-prabh-viral-videos-hq
| 2025-08-19T20:30:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:29:12Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
AnjaliNV/Merged_WellBeing_LLM
|
AnjaliNV
| 2025-08-19T20:29:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T20:23:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Muapi/mystic-vogue-80s-occult-pop
|
Muapi
| 2025-08-19T20:29:17Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:29:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Mystic Vogue 80s Occult Pop
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1227536@1383111", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755635310
|
Dejiat
| 2025-08-19T20:29:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:28:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/spacecraft
|
Muapi
| 2025-08-19T20:29:02Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:28:07Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# SpaceCraft
**Base model**: Flux.1 D
**Trained words**: SPCCRFT style.
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1182988@1750994", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
fatmhd1995/phi35_ft_llm_4_annotation_rnd3_v2
|
fatmhd1995
| 2025-08-19T20:26:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T20:22:19Z |
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** fatmhd1995
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
musdbi/model
|
musdbi
| 2025-08-19T20:26:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T20:25:01Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** musdbi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AnonymousCS/xlmr_immigration_combo1_1
|
AnonymousCS
| 2025-08-19T20:23:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T20:19:02Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo1_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo1_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2223
- Accuracy: 0.9216
- 1-f1: 0.8792
- 1-recall: 0.8571
- 1-precision: 0.9024
- Balanced Acc: 0.9055
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6512 | 1.0 | 25 | 0.5757 | 0.7237 | 0.2904 | 0.1699 | 1.0 | 0.5849 |
| 0.3902 | 2.0 | 50 | 0.3807 | 0.9100 | 0.8659 | 0.8726 | 0.8593 | 0.9006 |
| 0.3316 | 3.0 | 75 | 0.2580 | 0.9254 | 0.8835 | 0.8494 | 0.9205 | 0.9064 |
| 0.2921 | 4.0 | 100 | 0.2128 | 0.9267 | 0.8871 | 0.8649 | 0.9106 | 0.9112 |
| 0.2911 | 5.0 | 125 | 0.2406 | 0.9216 | 0.8820 | 0.8803 | 0.8837 | 0.9113 |
| 0.1527 | 6.0 | 150 | 0.2223 | 0.9216 | 0.8792 | 0.8571 | 0.9024 | 0.9055 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Tavernari/git-commit-message-splitter-Qwen3-14B
|
Tavernari
| 2025-08-19T20:20:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T19:35:26Z |
---
base_model: unsloth/qwen3-14b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Tavernari
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b
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)
|
a0a7/GreggRecognition
|
a0a7
| 2025-08-19T20:17:47Z | 0 | 1 | null |
[
"shorthand",
"code",
"stenography",
"steno",
"en",
"doi:10.57967/hf/5604",
"license:mit",
"region:us"
] | null | 2025-05-24T23:50:52Z |
---
license: mit
language:
- en
metrics:
- accuracy
tags:
- shorthand
- code
- stenography
- steno
---
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755634583
|
Dombili2038
| 2025-08-19T20:16:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:16:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist
|
bryanzhou008
| 2025-08-19T20:15:20Z | 60 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-30T19:48:56Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-inaturalist
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8541666666666666
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-inaturalist
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the inaturalist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7703
- Accuracy: 0.8542
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.8421 | 4 | 3.1793 | 0.0347 |
| No log | 1.8947 | 9 | 3.1647 | 0.0486 |
| 3.1648 | 2.9474 | 14 | 3.1382 | 0.0944 |
| 3.1648 | 4.0 | 19 | 3.0995 | 0.1556 |
| 3.0817 | 4.8421 | 23 | 3.0555 | 0.2639 |
| 3.0817 | 5.8947 | 28 | 2.9849 | 0.3889 |
| 2.9167 | 6.9474 | 33 | 2.8932 | 0.5139 |
| 2.9167 | 8.0 | 38 | 2.7775 | 0.5972 |
| 2.6682 | 8.8421 | 42 | 2.6706 | 0.6528 |
| 2.6682 | 9.8947 | 47 | 2.5233 | 0.7069 |
| 2.3659 | 10.9474 | 52 | 2.3859 | 0.7375 |
| 2.3659 | 12.0 | 57 | 2.2546 | 0.75 |
| 2.079 | 12.8421 | 61 | 2.1531 | 0.7528 |
| 2.079 | 13.8947 | 66 | 2.0372 | 0.75 |
| 1.828 | 14.9474 | 71 | 1.9339 | 0.7597 |
| 1.828 | 16.0 | 76 | 1.8403 | 0.7694 |
| 1.6253 | 16.8421 | 80 | 1.7733 | 0.7764 |
| 1.6253 | 17.8947 | 85 | 1.6914 | 0.7903 |
| 1.4502 | 18.9474 | 90 | 1.6153 | 0.7875 |
| 1.4502 | 20.0 | 95 | 1.5510 | 0.7986 |
| 1.4502 | 20.8421 | 99 | 1.5016 | 0.8 |
| 1.2959 | 21.8947 | 104 | 1.4454 | 0.8222 |
| 1.2959 | 22.9474 | 109 | 1.3912 | 0.8181 |
| 1.1802 | 24.0 | 114 | 1.3390 | 0.8333 |
| 1.1802 | 24.8421 | 118 | 1.2995 | 0.8333 |
| 1.0629 | 25.8947 | 123 | 1.2707 | 0.8389 |
| 1.0629 | 26.9474 | 128 | 1.2335 | 0.8361 |
| 0.9801 | 28.0 | 133 | 1.1975 | 0.8444 |
| 0.9801 | 28.8421 | 137 | 1.1672 | 0.8389 |
| 0.9076 | 29.8947 | 142 | 1.1338 | 0.8444 |
| 0.9076 | 30.9474 | 147 | 1.1137 | 0.8472 |
| 0.8349 | 32.0 | 152 | 1.0855 | 0.8528 |
| 0.8349 | 32.8421 | 156 | 1.0717 | 0.8542 |
| 0.7782 | 33.8947 | 161 | 1.0483 | 0.8514 |
| 0.7782 | 34.9474 | 166 | 1.0352 | 0.85 |
| 0.7208 | 36.0 | 171 | 1.0202 | 0.8556 |
| 0.7208 | 36.8421 | 175 | 0.9994 | 0.8486 |
| 0.6708 | 37.8947 | 180 | 0.9814 | 0.8556 |
| 0.6708 | 38.9474 | 185 | 0.9691 | 0.8542 |
| 0.6303 | 40.0 | 190 | 0.9599 | 0.8486 |
| 0.6303 | 40.8421 | 194 | 0.9422 | 0.8472 |
| 0.6303 | 41.8947 | 199 | 0.9278 | 0.8486 |
| 0.6018 | 42.9474 | 204 | 0.9172 | 0.8528 |
| 0.6018 | 44.0 | 209 | 0.9093 | 0.8514 |
| 0.5622 | 44.8421 | 213 | 0.9030 | 0.8583 |
| 0.5622 | 45.8947 | 218 | 0.8972 | 0.8625 |
| 0.5474 | 46.9474 | 223 | 0.8859 | 0.8569 |
| 0.5474 | 48.0 | 228 | 0.8858 | 0.8653 |
| 0.5254 | 48.8421 | 232 | 0.8779 | 0.8556 |
| 0.5254 | 49.8947 | 237 | 0.8635 | 0.8569 |
| 0.5036 | 50.9474 | 242 | 0.8563 | 0.8611 |
| 0.5036 | 52.0 | 247 | 0.8613 | 0.8542 |
| 0.4855 | 52.8421 | 251 | 0.8546 | 0.8625 |
| 0.4855 | 53.8947 | 256 | 0.8469 | 0.8597 |
| 0.4697 | 54.9474 | 261 | 0.8327 | 0.8528 |
| 0.4697 | 56.0 | 266 | 0.8268 | 0.8597 |
| 0.4482 | 56.8421 | 270 | 0.8188 | 0.8556 |
| 0.4482 | 57.8947 | 275 | 0.8171 | 0.8653 |
| 0.4436 | 58.9474 | 280 | 0.8133 | 0.8486 |
| 0.4436 | 60.0 | 285 | 0.8070 | 0.8639 |
| 0.4436 | 60.8421 | 289 | 0.7986 | 0.8542 |
| 0.4211 | 61.8947 | 294 | 0.7937 | 0.8597 |
| 0.4211 | 62.9474 | 299 | 0.7908 | 0.8611 |
| 0.4228 | 64.0 | 304 | 0.7952 | 0.8625 |
| 0.4228 | 64.8421 | 308 | 0.8010 | 0.8514 |
| 0.4046 | 65.8947 | 313 | 0.7975 | 0.8472 |
| 0.4046 | 66.9474 | 318 | 0.7927 | 0.8417 |
| 0.4048 | 68.0 | 323 | 0.7880 | 0.8556 |
| 0.4048 | 68.8421 | 327 | 0.7860 | 0.8514 |
| 0.3925 | 69.8947 | 332 | 0.7899 | 0.8403 |
| 0.3925 | 70.9474 | 337 | 0.7883 | 0.8417 |
| 0.3936 | 72.0 | 342 | 0.7885 | 0.8417 |
| 0.3936 | 72.8421 | 346 | 0.7874 | 0.8361 |
| 0.3985 | 73.8947 | 351 | 0.7832 | 0.8417 |
| 0.3985 | 74.9474 | 356 | 0.7787 | 0.8514 |
| 0.3849 | 76.0 | 361 | 0.7753 | 0.8486 |
| 0.3849 | 76.8421 | 365 | 0.7746 | 0.8514 |
| 0.3796 | 77.8947 | 370 | 0.7736 | 0.8542 |
| 0.3796 | 78.9474 | 375 | 0.7731 | 0.8528 |
| 0.3717 | 80.0 | 380 | 0.7715 | 0.8556 |
| 0.3717 | 80.8421 | 384 | 0.7709 | 0.8556 |
| 0.3717 | 81.8947 | 389 | 0.7706 | 0.8569 |
| 0.3802 | 82.9474 | 394 | 0.7704 | 0.8556 |
| 0.3802 | 84.0 | 399 | 0.7704 | 0.8542 |
| 0.3782 | 84.2105 | 400 | 0.7703 | 0.8542 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1
|
Muapi/mapdraw-flux
|
Muapi
| 2025-08-19T20:15:07Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:14:54Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# MapDraw (FLUX)
**Base model**: Flux.1 D
**Trained words**: m4pdr4w
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:777351@869395", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1755632890
|
koloni
| 2025-08-19T20:13:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:13:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/xlmr_immigration_combo1_0
|
AnonymousCS
| 2025-08-19T20:13:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T20:09:27Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo1_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo1_0
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2428
- Accuracy: 0.9190
- 1-f1: 0.8706
- 1-recall: 0.8185
- 1-precision: 0.9298
- Balanced Acc: 0.8939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2625 | 1.0 | 25 | 0.2291 | 0.9229 | 0.8760 | 0.8185 | 0.9422 | 0.8967 |
| 0.2154 | 2.0 | 50 | 0.2584 | 0.9177 | 0.8694 | 0.8224 | 0.9221 | 0.8939 |
| 0.1691 | 3.0 | 75 | 0.2428 | 0.9190 | 0.8706 | 0.8185 | 0.9298 | 0.8939 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
dfg1d2/sbi-gpt
|
dfg1d2
| 2025-08-19T20:04:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T20:04:08Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dfg1d2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Muapi/red-district
|
Muapi
| 2025-08-19T20:02:08Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:01:56Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Red District
**Base model**: Flux.1 D
**Trained words**: RedD3 style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1253652@1866718", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/lip-bite-concept-flux.1d
|
Muapi
| 2025-08-19T20:00:47Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:00:39Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Lip Bite Concept FLUX.1D
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:735399@1977332", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
video-filtrado-de-abigail-lalama/ver.filtracion.video.intimo.Abigail.Lalama.TikToker
|
video-filtrado-de-abigail-lalama
| 2025-08-19T19:58:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T19:56:56Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
|
Reynier/modernbert-dga-detector
|
Reynier
| 2025-08-19T18:23:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"domain-generation-algorithm",
"cybersecurity",
"domain-classification",
"security",
"malware-detection",
"en",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T18:22:40Z |
---
license: apache-2.0
tags:
- domain-generation-algorithm
- cybersecurity
- domain-classification
- security
- malware-detection
language:
- en
library_name: transformers
pipeline_tag: text-classification
base_model: answerdotai/ModernBERT-base
---
# ModernBERT DGA Detector
This model is designed to classify domains as either legitimate or generated by Domain Generation Algorithms (DGA).
## Model Description
- **Model Type:** BERT-based sequence classification
- **Task:** Binary classification (Legitimate vs DGA domains)
- **Base Model:** ModernBERT-base
- **Training Data:** Domain names dataset
- **Author:** Reynier Leyva La O
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Reynier/modernbert-dga-detector")
model = AutoModelForSequenceClassification.from_pretrained("Reynier/modernbert-dga-detector")
# Example prediction
def predict_domain(domain):
inputs = tokenizer(domain, return_tensors="pt", max_length=64, truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=-1)
legit_prob = predictions[0][0].item()
dga_prob = predictions[0][1].item()
return {"prediction": "DGA" if dga_prob > legit_prob else "LEGITIMATE",
"confidence": max(legit_prob, dga_prob)}
# Test examples
domains = ["google.com", "xkvbzpqr.net", "facebook.com", "abcdef123456.com"]
for domain in domains:
result = predict_domain(domain)
print(f"{domain} -> {result['prediction']} (confidence: {result['confidence']:.3f})")
```
## Model Architecture
The model is based on ModernBERT and fine-tuned for domain classification:
- Input: Domain names (text)
- Output: Binary classification (0=Legitimate, 1=DGA)
- Max sequence length: 64 tokens
## Training Details
This model was fine-tuned on a dataset of legitimate and DGA-generated domains using:
- Base model: answerdotai/ModernBERT-base
- Framework: Transformers/PyTorch
- Task: Binary sequence classification
## Performance
Add your model's performance metrics here when available:
- Accuracy: [Add your results]
- Precision: [Add your results]
- Recall: [Add your results]
- F1-Score: [Add your results]
## Use Cases
- **Cybersecurity**: Detect malicious domains generated by malware
- **Network Security**: Filter potentially harmful domains
- **Threat Intelligence**: Analyze domain patterns in security feeds
## Limitations
- This model is trained specifically for domain classification
- Performance may vary on domains from different TLDs or languages
- Regular retraining may be needed as DGA techniques evolve
- Model performance depends on the quality and diversity of training data
## Citation
If you use this model in your research or applications, please cite it appropriately.
## Related Models
Check out the author's other security models:
- [Llama3_8B-DGA-Detector](https://huggingface.co/Reynier/Llama3_8B-DGA-Detector)
|
OK923/wav2vec2-new_hindi_aug
|
OK923
| 2025-08-19T18:18:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-19T16:45:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AnonymousCS/xlmr_danish_immigration4
|
AnonymousCS
| 2025-08-19T18:17:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T18:15:14Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_danish_immigration4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_danish_immigration4
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3220
- Accuracy: 0.8615
- 1-f1: 0.7632
- 1-recall: 0.6744
- 1-precision: 0.8788
- Balanced Acc: 0.8142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3565 | 1.0 | 5 | 0.3175 | 0.8538 | 0.7467 | 0.6512 | 0.875 | 0.8026 |
| 0.3258 | 2.0 | 10 | 0.3383 | 0.8769 | 0.7778 | 0.6512 | 0.9655 | 0.8198 |
| 0.284 | 3.0 | 15 | 0.3220 | 0.8615 | 0.7632 | 0.6744 | 0.8788 | 0.8142 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
MrRikyz/Impish-Irix-Kitsune-GGUF
|
MrRikyz
| 2025-08-19T18:16:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"RP",
"mistral",
"roleplay",
"nsfw",
"llama-cpp",
"base_model:MrRikyz/Impish-Irix-Kitsune",
"base_model:quantized:MrRikyz/Impish-Irix-Kitsune",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T16:17:04Z |
---
base_model: MrRikyz/Impish-Irix-Kitsune
library_name: transformers
tags:
- mergekit
- merge
- RP
- mistral
- roleplay
- nsfw
- llama-cpp
license: apache-2.0
---
## About
static quants of https://huggingface.co/MrRikyz/Impish-Irix-Kitsune
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
If you want a specific quant just ask for it in the community tab
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q2_K.gguf) | Q2_K | 4.8 | Very low quality, Not recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_S.gguf) | Q3_K_S | 5.6 | low quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-IQ3_M.gguf) | IQ3_M | 5.7 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-IQ4_XS.gguf) | IQ4_XS | 6.8 | balanced speed and quality, recomended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q4_K_S.gguf) | Q4_K_S | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q4_K_M.gguf) | Q4_K_M | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q5_K_M.gguf) | Q5_K_M | 8.8 | good quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q6_K.gguf) | Q6_K | 10.1 | very good quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q8_0.gguf) | Q8_0 | 13.1 | best quality |
|
mohammadmahdinouri/moa-vanilla-checkpoints
|
mohammadmahdinouri
| 2025-08-19T18:14:36Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"ModernALBERT",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-19T11:45:52Z |
---
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]
|
yk0/forge-e39
|
yk0
| 2025-08-19T18:14:12Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2025-08-19T18:11:33Z |
# forge-v1 Model
Private testing version.
|
VIDEOS-19-tasnim-jara-viral-video-link/New.full.videos.tasnim.jara.Viral.Video.Official.Tutorial
|
VIDEOS-19-tasnim-jara-viral-video-link
| 2025-08-19T18:13:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T18:12:57Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
|
aumoai/aumogpt-Qwen2.5-32B-Instruct-lora
|
aumoai
| 2025-08-19T18:12:14Z | 67 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-05-08T15:53:49Z |
model:
model_name: "Qwen/Qwen2.5-32B-Instruct"
model_max_length: 4096
torch_dtype_str: "bfloat16"
attn_implementation: "flash_attention_2" #"sdpa"
load_pretrained_weights: True
trust_remote_code: True
data:
train:
datasets:
# - dataset_name: "text_sft"
# dataset_path: "datasets/aumo_dataset_test.json"
# shuffle: True
# seed: 42
- dataset_name: "text_sft"
dataset_path: "datasets/aumogpt_qwen32b.json"
shuffle: True
seed: 42
# - dataset_name: "text_sft"
# dataset_path: "datasets/xp3_qwen_2000.json"
# shuffle: True
# seed: 42
# - dataset_name: "text_sft"
# dataset_path: "datasets/aumogpt_train.json"
# shuffle: True
# seed: 42
# mixture_strategy: "all_exhausted" # Strategy for mixing datasets
# seed: 123456789426465
validation:
datasets:
- dataset_name: "text_sft"
dataset_path: "datasets/aumo_dataset_test.json"
# split: "validation"
# sample_count: 10
training:
trainer_type: "TRL_SFT"
use_peft: True
save_steps: 200
num_train_epochs: 2
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
gradient_accumulation_steps: 8
max_grad_norm: null
enable_gradient_checkpointing: True
gradient_checkpointing_kwargs:
use_reentrant: False
ddp_find_unused_parameters: False
optimizer: "adamw_torch" # "adamw_torch" #paged_adamw_8bit
learning_rate: 5.0e-4
warmup_steps: 10
weight_decay: 0.01
compile: False
dataloader_num_workers: 8
dataloader_prefetch_factor: 4
logging_steps: 10
log_model_summary: False
empty_device_cache_steps: 50
output_dir: "results/oumi/qwen32b_xp3_aumo.lora"
include_performance_metrics: True
enable_wandb: True
eval_strategy: "steps" # When to evaluate ("no", "steps", "epoch")
eval_steps: 25
peft:
q_lora: False
lora_r: 64
lora_alpha: 32
lora_dropout: 0.2
lora_target_modules:
- "q_proj"
- "k_proj"
- "v_proj"
- "o_proj"
- "gate_proj"
- "down_proj"
- "up_proj"
fsdp:
enable_fsdp: True
sharding_strategy: FULL_SHARD
auto_wrap_policy: TRANSFORMER_BASED_WRAP
# transformer_layer_cls: QwenBlock
forward_prefetch: true
|
chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755626507
|
chooseL1fe
| 2025-08-19T18:11:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny flightless albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:10:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny flightless albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AllOfWhich/vic
|
AllOfWhich
| 2025-08-19T18:10:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-08-19T18:09:11Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/df0r49x-0a00ace4-5e0b-4547-a453-d6f136b05cd1.png
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# vic
<Gallery />
## Download model
[Download](/AllOfWhich/vic/tree/main) them in the Files & versions tab.
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755626450
|
Dejiat
| 2025-08-19T18:01:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:01:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jerryzh168/Qwen3-8B-FP8
|
jerryzh168
| 2025-08-19T18:00:55Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"qwen3",
"text-generation",
"torchao",
"conversational",
"en",
"arxiv:2507.16099",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T18:00:02Z |
---
base_model: Qwen/Qwen3-8B
tags:
- transformers
- torchao
- qwen3
license: apache-2.0
language:
- en
---
# FP8 Qwen/Qwen3-8B model
- **Developed by:** jerryzh168
- **License:** apache-2.0
- **Quantized from Model :** Qwen/Qwen3-8B
- **Quantization Method :** FP8
# Inference with vLLM
Install vllm nightly and torchao nightly to get some recent changes:
```
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
pip install torchao
```
## Serving
Then we can serve with the following command:
```Shell
# Server
export MODEL=jerryzh168/Qwen3-8B-FP8
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3
```
```Shell
# Client
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "jerryzh168/Qwen3-8B-FP8",
"messages": [
{"role": "user", "content": "Give me a short introduction to large language models."}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 32768
}'
```
Note: please use `VLLM_DISABLE_COMPILE_CACHE=1` to disable compile cache when running this code, e.g. `VLLM_DISABLE_COMPILE_CACHE=1 python example.py`, since there are some issues with the composability of compile in vLLM and torchao,
this is expected be resolved in pytorch 2.8.
# Inference with Transformers
Install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install torchao
pip install torch
pip install accelerate
```
Example:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jerryzh168/Qwen3-8B-FP8"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("
")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("
")
print("thinking content:", thinking_content)
print("content:", content)
```
# Quantization Recipe
Install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
pip install torch
pip install accelerate
```
Use the following code to get the quantized model:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "Qwen/Qwen3-8B"
model_to_quantize = "Qwen/Qwen3-8B"
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow
quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_to_quantize, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Push to hub
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-FP8"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)
# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
```
Note: to `push_to_hub` you need to run
```Shell
pip install -U "huggingface_hub[cli]"
huggingface-cli login
```
and use a token with write access, from https://huggingface.co/settings/tokens
# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model. Here we only run on mmlu for sanity check.
| Benchmark | | |
|----------------------------------|----------------|---------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-FP8 |
| mmlu | To be filled | To be filled |
<details>
<summary> Reproduce Model Quality Results </summary>
Need to install lm-eval from source:
https://github.com/EleutherAI/lm-evaluation-harness#install
## baseline
```Shell
lm_eval --model hf --model_args pretrained=Qwen/Qwen3-8B --tasks mmlu --device cuda:0 --batch_size 8
```
## int4 weight only quantization with hqq (INT4)
```Shell
export MODEL=jerryzh168/Qwen3-8B-FP8
lm_eval --model hf --model_args pretrained=$MODEL --tasks mmlu --device cuda:0 --batch_size 8
```
</details>
# Peak Memory Usage
## Results
| Benchmark | | |
|------------------|----------------|--------------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-FP8 |
| Peak Memory (GB) | To be filled | To be filled (?% reduction) |
<details>
<summary> Reproduce Peak Memory Usage Results </summary>
We can use the following code to get a sense of peak memory usage during inference:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
# use "Qwen/Qwen3-8B" or "jerryzh168/Qwen3-8B-FP8"
model_id = "jerryzh168/Qwen3-8B-FP8"
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
torch.cuda.reset_peak_memory_stats()
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Peak Memory Usage: {mem:.02f} GB")
```
</details>
# Model Performance
## Results (A100 machine)
| Benchmark (Latency) | | |
|----------------------------------|----------------|--------------------------|
| | Qwen/Qwen3-8B | jerryzh168/Qwen3-8B-FP8 |
| latency (batch_size=1) | ?s | ?s (?x speedup) |
<details>
<summary> Reproduce Model Performance Results </summary>
## Setup
Get vllm source code:
```Shell
git clone git@github.com:vllm-project/vllm.git
```
Install vllm
```
VLLM_USE_PRECOMPILED=1 pip install --editable .
```
Run the benchmarks under `vllm` root folder:
## benchmark_latency
### baseline
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
### INT4
```Shell
export MODEL=jerryzh168/Qwen3-8B-FP8
VLLM_DISABLE_COMPILE_CACHE=1 python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
## benchmark_serving
We benchmarked the throughput in a serving environment.
Download sharegpt dataset:
```Shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
Note: you can change the number of prompts to be benchmarked with `--num-prompts` argument for `benchmark_serving` script.
### baseline
Server:
```Shell
export MODEL=Qwen/Qwen3-8B
vllm serve $MODEL --tokenizer $MODEL -O3
```
Client:
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
### FP8
Server:
```Shell
export MODEL=jerryzh168/Qwen3-8B-FP8
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3 --pt-load-map-location cuda:0
```
Client:
```Shell
export MODEL=jerryzh168/Qwen3-8B-FP8
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
</details>
# Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).
**Abstract:** We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .
# Resources
* **Official TorchAO GitHub Repository:** [https://github.com/pytorch/ao](https://github.com/pytorch/ao)
* **TorchAO Documentation:** [https://docs.pytorch.org/ao/stable/index.html](https://docs.pytorch.org/ao/stable/index.html)
# Disclaimer
PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.
|
Najin06/esp32-misterius
|
Najin06
| 2025-08-19T17:57:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T06:10:24Z |
---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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|
MrRikyz/Impish-Irix-Kitsune
|
MrRikyz
| 2025-08-19T17:56:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"RP",
"roleplay",
"NSFW",
"conversational",
"arxiv:2403.19522",
"base_model:DreadPoor/Irix-12B-Model_Stock",
"base_model:merge:DreadPoor/Irix-12B-Model_Stock",
"base_model:MrRikyz/Kitsune-Symphony-V0.0-12B",
"base_model:merge:MrRikyz/Kitsune-Symphony-V0.0-12B",
"base_model:SicariusSicariiStuff/Impish_Nemo_12B",
"base_model:merge:SicariusSicariiStuff/Impish_Nemo_12B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:37:59Z |
---
base_model:
- DreadPoor/Irix-12B-Model_Stock
- MrRikyz/Kitsune-Symphony-V0.0-12B
- SicariusSicariiStuff/Impish_Nemo_12B
library_name: transformers
tags:
- mergekit
- merge
- RP
- roleplay
- NSFW
license: apache-2.0
---
# merged
# 🛑 Premise
Alright so. Here we are again with my second merge, this time i used [Model Stock](https://arxiv.org/abs/2403.19522) as the merging method and SicariusSicariiStuff/Impish_Nemo_12B as a base since I tried it and I really liked it
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
# Data
well... i didn't add any new data so nothing, I just merged the models
# 🧪 Intended Use
This model is designed mainly for:
- Roleplay and creative writing
- Storytelling & world-building
# 😷 Ethical Containment
This model can:
- ⚠️ Generating unfiltered creative content
- ⚠️ Producing potentially disturbing narratives
- ⚠️ Creating NSFW content
# ⚖️ License
Follow the licensing terms of each merged model:
Each source model’s license applies
## Merge Details
### Models Merged
The following models were included in the merge:
* DreadPoor/Irix-12B-Model_Stock
* MrRikyz/Kitsune-Symphony-V0.0-12B
* SicariusSicariiStuff/Impish_Nemo_12B
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: SicariusSicariiStuff/Impish_Nemo_12B
dtype: float16
merge_method: model_stock
modules:
default:
slices:
- sources:
- layer_range: [0, 40]
model: MrRikyz/Kitsune-Symphony-V0.0-12B
parameters:
weight: 0.35
- layer_range: [0, 40]
model: SicariusSicariiStuff/Impish_Nemo_12B
parameters:
weight: 0.55
- layer_range: [0, 40]
model: DreadPoor/Irix-12B-Model_Stock
parameters:
weight: 0.3
tokenizer:
source: base
```
# ✨ Acknowledgements
Thanks to the authors of the original models for their incredible work:
- SicariusSicariiStuff for Impish Nemo 12B (Great model I really liked it)
- DreadPoor for Irix 12B
- MrRikyz for Kitsune-Symphony (Myself)
|
khawarizmiai/Khawarizmi-SPI-MLP-8B
|
khawarizmiai
| 2025-08-19T17:55:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T12:47:42Z |
---
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
license: mit
---
# Khawarizmi-SPI-MLP-8B
## Model Overview
**Khawarizmi-SPI-MLP-8B** is a hybrid language model developed by Khawarizmi AI, leveraging the innovative **Selective Parameter Interpolation on MLP Layers (SPI-MLP)** algorithm. This model ingeniously combines the robust linguistic capabilities of **Qwen3-8B** with the advanced reasoning patterns of **DeepSeek-R1**. The fusion is specifically applied to the Multi-Layer Perceptron (MLP) layers, with a composition of 60% DeepSeek and 40% Qwen, while critically preserving Qwen's original attention and normalization layers. This unique architectural approach aims to deliver a model highly proficient in complex reasoning, code generation, and multilingual tasks, with a particular emphasis on Arabic-English understanding.
## Technical Specifications
Based on the `config.json` and `generation_config.json` files, the Khawarizmi-SPI-MLP-8B model exhibits the following technical characteristics:
### Architecture and Configuration
| Parameter | Value | Description |
|---|---|---|
| `architectures` | `["Qwen3ForCausalLM"]` | Indicates the model architecture is a Causal Language Model based on Qwen3. |
| `attention_bias` | `false` | Specifies if attention bias is used. |
| `attention_dropout` | `0.0` | Dropout rate for attention layers. |
| `bos_token_id` | `151643` | Beginning-of-sequence token ID. |
| `eos_token_id` | `[151645, 151643]` | End-of-sequence token IDs. |
| `head_dim` | `128` | Dimension of each attention head. |
| `hidden_act` | `"silu"` | Activation function used in hidden layers. |
| `hidden_size` | `4096` | Dimensionality of the encoder layers and the pooler layer. |
| `initializer_range` | `0.02` | Standard deviation of the truncated normal initializer. |
| `intermediate_size` | `12288` | Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. |
| `max_position_embeddings` | `40960` | The maximum sequence length that this model might ever be used with. |
| `max_window_layers` | `36` | Maximum number of layers for windowed attention. |
| `model_type` | `"qwen3"` | The type of the model, indicating its base family. |
| `num_attention_heads` | `32` | Number of attention heads for each attention layer in the Transformer encoder. |
| `num_hidden_layers` | `36` | Number of hidden layers in the Transformer encoder. |
| `num_key_value_heads` | `8` | Number of key-value heads. |
| `rms_norm_eps` | `1e-06` | The epsilon used by the RMS normalization layers. |
| `rope_scaling` | `null` | RoPE scaling configuration. |
| `rope_theta` | `1000000` | RoPE theta value. |
| `sliding_window` | `null` | Sliding window configuration. |
| `tie_word_embeddings` | `false` | Whether to tie the word embeddings with the output layer. |
| `torch_dtype` | `"bfloat16"` | The data type used for the model parameters. |
| `transformers_version` | `"4.51.0"` | The version of the Hugging Face Transformers library used. |
| `use_cache` | `true` | Whether or not the model should return the last key/values attentions (not used by all models). |
| `use_sliding_window` | `false` | Whether to use sliding window attention. |
| `vocab_size` | `151936` | Vocabulary size of the model. |
### Generation Configuration
| Parameter | Value | Description |
|---|---|---|
| `do_sample` | `true` | Whether or not to use sampling; use greedy decoding otherwise. |
| `pad_token_id` | `151643` | Padding token ID. |
| `temperature` | `0.6` | The value used to modulate the next token probabilities. |
| `top_k` | `20` | The number of highest probability vocabulary tokens to keep for top-k-filtering. |
| `top_p` | `0.95` | If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation. |
| `transformers_version` | `"4.51.0"` | The version of the Hugging Face Transformers library used for generation. |
## Merge Strategy
The **Khawarizmi-SPI-MLP-8B** model employs a sophisticated Selective Parameter Interpolation (SPI) strategy specifically targeting the MLP layers. This method allows for a nuanced integration of two distinct models: **Qwen3-8B** and **DeepSeek-R1**. The core idea is to selectively interpolate parameters within the MLP layers, achieving a blend that harnesses the strengths of both base models while maintaining the structural integrity of Qwen's attention and normalization layers. This approach ensures that the model benefits from DeepSeek-R1's reasoning capabilities without compromising Qwen3-8B's established linguistic prowess.
For each weight tensor $W_k$:
$$
W_k^{\text{merged}} =
\begin{cases}
0.6 \cdot W_k^{\text{(DeepSeek)}} + 0.4 \cdot W_k^{\text{(Qwen)}} & \text{if "mlp" in } k \\
W_k^{\text{(Qwen)}} & \text{otherwise}
\end{cases}
$$
## How to Use
To utilize the **Khawarizmi-SPI-MLP-8B** model, follow the instructions below. Ensure you have the necessary dependencies installed.
### Installation
First, install the required Python packages using `pip`:
```bash
pip install -q transformers accelerate safetensors sentencepiece torch
```
### Model Loading and Inference
Once the dependencies are installed, you can load the model and tokenizer using the Hugging Face `transformers` library and perform text generation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "khawarizmiai/Khawarizmi-SPI-MLP-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
# Example usage (you can expand on this with more detailed examples)
# prompt = "Write a short story about a robot learning to feel."
# input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
# generated_ids = model.generate(**input_ids, max_new_tokens=100)
# print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
## Evaluation
While specific benchmark results for Khawarizmi-SPI-MLP-8B are not detailed in the provided files, the model's design, which integrates DeepSeek-R1's reasoning patterns, suggests a focus on improving performance in areas such as:
* **Reasoning**: Enhanced logical reasoning and problem-solving capabilities.
* **Code Generation**: Improved ability to generate accurate and efficient code.
* **Multilingual Tasks**: Stronger performance in understanding and generating text in multiple languages, particularly Arabic and English.
Further evaluations would be necessary to quantify the model's performance across standard benchmarks (e.g., MMLU, GSM8K, HumanEval) to provide a comprehensive understanding of its capabilities.
## Limitations
As with all large language models, Khawarizmi-SPI-MLP-8B may exhibit certain limitations inherent to current AI technology:
* **Hallucination**: The model might generate factually incorrect or nonsensical information.
* **Bias**: Potential biases present in the training data could be reflected in the model's outputs.
* **Lack of Common Sense**: The model may occasionally lack human-like common sense reasoning, leading to unexpected or illogical responses.
Users are advised to exercise caution and verify critical information generated by the model.
## License
The licensing information for Khawarizmi-SPI-MLP-8B is available in the `LICENSE` file within the repository. Users should refer to this file for detailed terms and conditions regarding the use and distribution of the model.
## Citation
If you find Khawarizmi-SPI-MLP-8B useful in your research or applications, please consider citing it. A formal citation will be provided upon publication of the research paper detailing the SPI-MLP algorithm and the model's development.
## Acknowledgements
We extend our gratitude to the open-source community and the developers of Qwen3-8B and DeepSeek-R1, whose foundational work has been instrumental in the creation of Khawarizmi-SPI-MLP-8B. Their contributions continue to drive innovation in the field of artificial intelligence.
## Contact
For inquiries, collaborations, or feedback regarding Khawarizmi-SPI-MLP-8B, please reach out to the Khawarizmi AI team through the Hugging Face platform or official channels as they become available.
## Disclaimer
Khawarizmi-SPI-MLP-8B is provided for research and experimental purposes. While efforts have been made to ensure its quality and performance, Khawarizmi AI does not guarantee its suitability for any specific application. Users are responsible for assessing the model's outputs and ensuring compliance with all applicable laws and regulations.
live
|
ver-intimo-video-de-abigail-lalama/video.de.abigail.lalama.y.snayder.influencer.se.hace.viral.en.redes
|
ver-intimo-video-de-abigail-lalama
| 2025-08-19T17:54:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T17:54:01Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
|
fhalation/zephyr-7b-dpo-qlora
|
fhalation
| 2025-08-19T17:51:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dpo",
"trl",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T07:27:22Z |
---
library_name: transformers
model_name: zephyr-7b-dpo-qlora
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for zephyr-7b-dpo-qlora
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="fhalation/zephyr-7b-dpo-qlora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
VIDEOS-19-afrin-apu-viral-link/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial
|
VIDEOS-19-afrin-apu-viral-link
| 2025-08-19T17:51:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T17:51:41Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-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>
|
prerana17/testmodel
|
prerana17
| 2025-08-19T17:51:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T17:48:25Z |
---
base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** prerana17
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755624343
|
mang3dd
| 2025-08-19T17:51:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:51:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
PetraBevandic/vlm-tutorial-finetuned-llm
|
PetraBevandic
| 2025-08-19T17:44:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolVLM-256M-Base",
"base_model:finetune:HuggingFaceTB/SmolVLM-256M-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:34:35Z |
---
base_model: HuggingFaceTB/SmolVLM-256M-Base
library_name: transformers
model_name: vlm-tutorial-finetuned-llm
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for vlm-tutorial-finetuned-llm
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-256M-Base](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="PetraBevandic/vlm-tutorial-finetuned-llm", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
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