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
|
|---|---|---|---|---|---|---|---|---|---|
seraphimzzzz/46927
|
seraphimzzzz
| 2025-08-19T23:03:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:03:17Z |
[View on Civ Archive](https://civarchive.com/models/62508?modelVersionId=67059)
|
seraphimzzzz/105764
|
seraphimzzzz
| 2025-08-19T23:01:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:01:56Z |
[View on Civ Archive](https://civarchive.com/models/130755?modelVersionId=143521)
|
ultratopaz/87314
|
ultratopaz
| 2025-08-19T23:01:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:01:49Z |
[View on Civ Archive](https://civarchive.com/models/112043?modelVersionId=120937)
|
seraphimzzzz/51823
|
seraphimzzzz
| 2025-08-19T23:01:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:01:00Z |
[View on Civ Archive](https://civarchive.com/models/68607?modelVersionId=75049)
|
crystalline7/67939
|
crystalline7
| 2025-08-19T23:00:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:52Z |
[View on Civ Archive](https://civarchive.com/models/91623?modelVersionId=97665)
|
seraphimzzzz/10107
|
seraphimzzzz
| 2025-08-19T23:00:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:35Z |
[View on Civ Archive](https://civarchive.com/models/9092?modelVersionId=10747)
|
ver-video-intimo-de-Clip-abigail-lalama/ver-video-intimo-de-Clip-abigail-lalama
|
ver-video-intimo-de-Clip-abigail-lalama
| 2025-08-19T23:00:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:15Z |
<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>
|
crystalline7/100394
|
crystalline7
| 2025-08-19T23:00:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:09Z |
[View on Civ Archive](https://civarchive.com/models/125655?modelVersionId=137280)
|
seraphimzzzz/535022
|
seraphimzzzz
| 2025-08-19T22:59:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:59:05Z |
[View on Civ Archive](https://civarchive.com/models/462107?modelVersionId=620069)
|
ultratopaz/80643
|
ultratopaz
| 2025-08-19T22:58:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:58:03Z |
[View on Civ Archive](https://civarchive.com/models/105746?modelVersionId=113516)
|
seraphimzzzz/26100
|
seraphimzzzz
| 2025-08-19T22:57:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:57:22Z |
[View on Civ Archive](https://civarchive.com/models/26393?modelVersionId=31601)
|
ultratopaz/66429
|
ultratopaz
| 2025-08-19T22:56:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:56:50Z |
[View on Civ Archive](https://civarchive.com/models/89936?modelVersionId=95772)
|
crystalline7/16814
|
crystalline7
| 2025-08-19T22:56:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:55Z |
[View on Civ Archive](https://civarchive.com/models/17067?modelVersionId=20153)
|
GeneroGral/Mistral-Nemo-12B_BBQ_Stereo_MERGED7_dropout_batch-wordMatch
|
GeneroGral
| 2025-08-19T22:55:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:finetune:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T22:51:20Z |
---
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GeneroGral
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Base-2407
This mistral 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)
|
Bearrr310/ds_train_grpo_1.5B-0818-dsvllm-acc32
|
Bearrr310
| 2025-08-19T22:55:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"qwen2",
"text-generation",
"generated_from_trainer",
"grpo",
"trl",
"conversational",
"dataset:ds_train_grpo_1.5B-0818-dsvllm-acc32",
"arxiv:2402.03300",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T10:52:28Z |
---
datasets: ds_train_grpo_1.5B-0818-dsvllm-acc32
library_name: transformers
model_name: ds_train_grpo_1.5B-0818-dsvllm-acc32
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for ds_train_grpo_1.5B-0818-dsvllm-acc32
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [ds_train_grpo_1.5B-0818-dsvllm-acc32](https://huggingface.co/datasets/ds_train_grpo_1.5B-0818-dsvllm-acc32) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Bearrr310/ds_train_grpo_1.5B-0818-dsvllm-acc32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ultratopaz/59092
|
ultratopaz
| 2025-08-19T22:55:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:09Z |
[View on Civ Archive](https://civarchive.com/models/81480?modelVersionId=86456)
|
ultratopaz/38152
|
ultratopaz
| 2025-08-19T22:55:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:02Z |
[View on Civ Archive](https://civarchive.com/models/47805?modelVersionId=52399)
|
crystalline7/36455
|
crystalline7
| 2025-08-19T22:54:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:54:42Z |
[View on Civ Archive](https://civarchive.com/models/44884?modelVersionId=49503)
|
ultratopaz/24443
|
ultratopaz
| 2025-08-19T22:52:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:44Z |
[View on Civ Archive](https://civarchive.com/models/24707?modelVersionId=29556)
|
crystalline7/38157
|
crystalline7
| 2025-08-19T22:52:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:33Z |
[View on Civ Archive](https://civarchive.com/models/47820?modelVersionId=52412)
|
AnonymousCS/xlmr_immigration_combo6_3
|
AnonymousCS
| 2025-08-19T22:52:26Z | 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
| 2025-08-19T22:48:23Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo6_3
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_combo6_3
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: 0.2175
- Accuracy: 0.9293
- 1-f1: 0.8968
- 1-recall: 0.9228
- 1-precision: 0.8723
- Balanced Acc: 0.9277
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1432 | 1.0 | 25 | 0.2095 | 0.9242 | 0.8921 | 0.9421 | 0.8472 | 0.9287 |
| 0.0923 | 2.0 | 50 | 0.1510 | 0.9563 | 0.9328 | 0.9112 | 0.9555 | 0.9450 |
| 0.1044 | 3.0 | 75 | 0.1735 | 0.9524 | 0.9284 | 0.9266 | 0.9302 | 0.9460 |
| 0.1035 | 4.0 | 100 | 0.2175 | 0.9293 | 0.8968 | 0.9228 | 0.8723 | 0.9277 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
crystalline7/25213
|
crystalline7
| 2025-08-19T22:52:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:11Z |
[View on Civ Archive](https://civarchive.com/models/25513?modelVersionId=30545)
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755642210
|
coelacanthxyz
| 2025-08-19T22:52:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:52:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/85990
|
ultratopaz
| 2025-08-19T22:52:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:02Z |
[View on Civ Archive](https://civarchive.com/models/39509?modelVersionId=119927)
|
seraphimzzzz/34079
|
seraphimzzzz
| 2025-08-19T22:51:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:51:51Z |
[View on Civ Archive](https://civarchive.com/models/39509?modelVersionId=45600)
|
seraphimzzzz/25253
|
seraphimzzzz
| 2025-08-19T22:51:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:51:06Z |
[View on Civ Archive](https://civarchive.com/models/19239?modelVersionId=30605)
|
seraphimzzzz/19008
|
seraphimzzzz
| 2025-08-19T22:50:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:50:55Z |
[View on Civ Archive](https://civarchive.com/models/19239?modelVersionId=22829)
|
GeneroGral/Mistral-Nemo-12B_BBQ_Stereo6_dropout_batch-wordMatch
|
GeneroGral
| 2025-08-19T22:50:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:finetune:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T22:50:24Z |
---
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GeneroGral
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Base-2407
This mistral 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)
|
seraphimzzzz/47559
|
seraphimzzzz
| 2025-08-19T22:50:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:49:57Z |
[View on Civ Archive](https://civarchive.com/models/63423?modelVersionId=67976)
|
crystalline7/81271
|
crystalline7
| 2025-08-19T22:49:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:49:37Z |
[View on Civ Archive](https://civarchive.com/models/17612?modelVersionId=114290)
|
crystalline7/88151
|
crystalline7
| 2025-08-19T22:48:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:48:10Z |
[View on Civ Archive](https://civarchive.com/models/113358?modelVersionId=122445)
|
zhuojing-huang/gpt2-portuguese-english-ewc-2
|
zhuojing-huang
| 2025-08-19T22:47:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T10:29:22Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt2-portuguese-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-portuguese-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
|
crystalline7/99930
|
crystalline7
| 2025-08-19T22:47:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:47:09Z |
[View on Civ Archive](https://civarchive.com/models/125177?modelVersionId=136725)
|
seraphimzzzz/22894
|
seraphimzzzz
| 2025-08-19T22:46:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:46:29Z |
[View on Civ Archive](https://civarchive.com/models/23181?modelVersionId=27689)
|
seraphimzzzz/48040
|
seraphimzzzz
| 2025-08-19T22:46:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:46:21Z |
[View on Civ Archive](https://civarchive.com/models/64095?modelVersionId=68699)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755643544
|
lilTAT
| 2025-08-19T22:46:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:46:07Z |
---
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).
|
crystalline7/55294
|
crystalline7
| 2025-08-19T22:46:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:46:00Z |
[View on Civ Archive](https://civarchive.com/models/75907?modelVersionId=80642)
|
seraphimzzzz/100080
|
seraphimzzzz
| 2025-08-19T22:45:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:45:52Z |
[View on Civ Archive](https://civarchive.com/models/125326?modelVersionId=136894)
|
crystalline7/48143
|
crystalline7
| 2025-08-19T22:45:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:45:20Z |
[View on Civ Archive](https://civarchive.com/models/64265?modelVersionId=68851)
|
seraphimzzzz/32237
|
seraphimzzzz
| 2025-08-19T22:44:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:44:51Z |
[View on Civ Archive](https://civarchive.com/models/35827?modelVersionId=42023)
|
ultratopaz/6908
|
ultratopaz
| 2025-08-19T22:44:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:44:41Z |
[View on Civ Archive](https://civarchive.com/models/5764?modelVersionId=6719)
|
seraphimzzzz/770787
|
seraphimzzzz
| 2025-08-19T22:44:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:44:26Z |
[View on Civ Archive](https://civarchive.com/models/769368?modelVersionId=860516)
|
ultratopaz/61978
|
ultratopaz
| 2025-08-19T22:43:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:43:56Z |
[View on Civ Archive](https://civarchive.com/models/80680?modelVersionId=90116)
|
crystalline7/54021
|
crystalline7
| 2025-08-19T22:43:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:43:43Z |
[View on Civ Archive](https://civarchive.com/models/73874?modelVersionId=78592)
|
seraphimzzzz/64825
|
seraphimzzzz
| 2025-08-19T22:43:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:43:17Z |
[View on Civ Archive](https://civarchive.com/models/88118?modelVersionId=93781)
|
ultratopaz/55596
|
ultratopaz
| 2025-08-19T22:43:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:43:07Z |
[View on Civ Archive](https://civarchive.com/models/76316?modelVersionId=81089)
|
ultratopaz/85909
|
ultratopaz
| 2025-08-19T22:42:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:42:44Z |
[View on Civ Archive](https://civarchive.com/models/23644?modelVersionId=119839)
|
AnonymousCS/xlmr_immigration_combo6_2
|
AnonymousCS
| 2025-08-19T22:42:16Z | 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
| 2025-08-19T22:38:15Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo6_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. -->
# xlmr_immigration_combo6_2
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: 0.2027
- Accuracy: 0.9357
- 1-f1: 0.9038
- 1-recall: 0.9073
- 1-precision: 0.9004
- Balanced Acc: 0.9286
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.216 | 1.0 | 25 | 0.1942 | 0.9344 | 0.9054 | 0.9421 | 0.8714 | 0.9364 |
| 0.1415 | 2.0 | 50 | 0.1577 | 0.9499 | 0.9234 | 0.9073 | 0.94 | 0.9392 |
| 0.2224 | 3.0 | 75 | 0.2337 | 0.9242 | 0.8913 | 0.9344 | 0.8521 | 0.9267 |
| 0.0914 | 4.0 | 100 | 0.2027 | 0.9357 | 0.9038 | 0.9073 | 0.9004 | 0.9286 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
seraphimzzzz/74022
|
seraphimzzzz
| 2025-08-19T22:40:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:40:55Z |
[View on Civ Archive](https://civarchive.com/models/98486?modelVersionId=105327)
|
crystalline7/33987
|
crystalline7
| 2025-08-19T22:40:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:40:23Z |
[View on Civ Archive](https://civarchive.com/models/39434?modelVersionId=45341)
|
crystalline7/32802
|
crystalline7
| 2025-08-19T22:40:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:40:15Z |
[View on Civ Archive](https://civarchive.com/models/36916?modelVersionId=42949)
|
ultratopaz/67456
|
ultratopaz
| 2025-08-19T22:40:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:40:06Z |
[View on Civ Archive](https://civarchive.com/models/16450?modelVersionId=97044)
|
ultratopaz/83740
|
ultratopaz
| 2025-08-19T22:39:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:39:34Z |
[View on Civ Archive](https://civarchive.com/models/108800?modelVersionId=117184)
|
rvs/llama3_awq_int4_complete
|
rvs
| 2025-08-19T22:39:35Z | 0 | 0 | null |
[
"onnx",
"text-generation-inference",
"llama",
"llama3",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2025-08-19T22:39:00Z |
---
tags:
- text-generation-inference
- llama
- llama3
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
---
# Llama 3 8B Instruct with Key-Value-Cache enabled in ONNX ONNX AWQ (4-bit) format
- Model creator: [Meta Llama](https://huggingface.co/meta-llama)
- Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
<!-- description start -->
## Description
This repo contains the ONNX files for the ONNX conversion of Llama 3 8B Instruct done by Esperanto Technologies.
The model is in the 4-bit format quantized with AWQ and has the KVC enabled.
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
More here: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)
<!-- description end -->
## How to download ONNX model and weight files
The easiest way to obtain the model is to clone this whole repo.
Alternatively you can download the files is using the `huggingface-hub` Python library.
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir-use-symlinks False
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
## How to run from Python code using ONNXRuntime
This model can easily be ran in a CPU using [ONNXRuntime](https://onnxruntime.ai/).
#### First install the packages
```bash
pip3 install onnx==1.16.1
pip3 install onnxruntime==1.17.1
```
#### Example code: generate text with this model
We define the loop with greedy decoding:
```python
import numpy as np
import onnxruntime
import onnx
from transformers import AutoTokenizer
def generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context):
model = onnx.load(model_path)
#we create the inputs for the first iteration
input_tensor = tokenizer(prompt, return_tensors="pt")
prompt_size = len(input_tensor['input_ids'][0])
actual_input = input_tensor['input_ids']
if prompt_size < window:
actual_input = np.concatenate((tokenizer.bos_token_id*np.ones([1, window - prompt_size], dtype = 'int64'),
actual_input), axis=1)
if prompt_size + max_gen_tokens > total_sequence:
print("ERROR: Longer total sequence is needed!")
return
first_attention = np.concatenate((np.zeros([1, total_sequence - window], dtype = 'int64'),
np.ones((1, window), dtype = 'int64')), axis=1)
max_gen_tokens += prompt_size #we need to generate on top of parsing the prompt
inputs_names =[node.name for node in model.graph.input]
output_names =[node.name for node in model.graph.output]
n_heads = 8 #gqa-heads of the kvc
inputs_dict = {}
inputs_dict['input_ids'] = actual_input[:, :window].reshape(1, window).numpy()
inputs_dict['attention_mask'] = first_attention
index_pos = sum(first_attention[0])
inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - index_pos], dtype = 'int64'), np.arange(index_pos, dtype = 'int64').reshape(1, index_pos)), axis=1)
inputs_dict['tree_attention'] = np.triu(-65504*np.ones(total_sequence), k= 1).astype('float16').reshape(1, 1, total_sequence, total_sequence)
for name in inputs_names:
if name == 'input_ids' or name == 'attention_mask' or name == 'position_ids' or name == 'tree_attention': continue
inputs_dict[name] = np.zeros([1, n_heads, context-window, 128], dtype="float16")
index = 0
new_token = np.array([10])
next_index = window
old_j = 0
total_input = actual_input.numpy()
rt_session = onnxruntime.InferenceSession(model_path)
## We run the inferences
while next_index < max_gen_tokens:
if new_token.any() == tokenizer.eos_token_id:
break
#inference
output = rt_session.run(output_names, inputs_dict)
outs_dictionary = {name: content for (name, content) in zip (output_names, output)}
#we prepare the inputs for the next inference
for name in inputs_names:
if name == 'input_ids':
old_j = next_index
if next_index < prompt_size:
if prompt_size - next_index >= window: next_index += window
else: next_index = prompt_size
j = next_index - window
else:
next_index +=1
j = next_index - window
new_token = outs_dictionary['logits'].argmax(-1).reshape(1, window)
total_input = np.concatenate((total_input, new_token[: , -1:]), axis = 1)
inputs_dict['input_ids']= total_input[:, j:next_index].reshape(1, window)
elif name == 'attention_mask':
inputs_dict['attention_mask'] = np.concatenate((np.zeros((1, total_sequence-next_index), dtype = 'int64'), np.ones((1, next_index), dtype = 'int64')), axis=1)
elif name == 'position_ids':
inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - next_index], dtype = 'int64'), np.arange(next_index, dtype = 'int64').reshape(1, next_index)), axis=1)
elif name == 'tree_attention': continue
else:
old_name = name.replace("past_key_values", "present")
inputs_dict[name] = outs_dictionary[old_name][:, :, next_index-old_j:context-window+(next_index - old_j), :]
answer = tokenizer.decode(total_input[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
return answer
```
We now run the inferences:
```python
tokenizer = AutoTokenizer.from_pretrained("Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx-onnx")
model_path = "llama3-8b-Instruct-kvc-AWQ-int4-onnx/model.onnx"
max_gen_tokens = 20 #number of tokens we want tog eneral
total_sequence = 128 #total sequence_length
context = 1024 #the context to extend the kvc
window = 16 #number of tokens we want to parse at the time
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
generated = generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context)
print(generated)
```
|
seraphimzzzz/43542
|
seraphimzzzz
| 2025-08-19T22:39:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:39:26Z |
[View on Civ Archive](https://civarchive.com/models/57158?modelVersionId=61571)
|
seraphimzzzz/393652
|
seraphimzzzz
| 2025-08-19T22:38:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:38:35Z |
[View on Civ Archive](https://civarchive.com/models/424129?modelVersionId=475119)
|
seraphimzzzz/635835
|
seraphimzzzz
| 2025-08-19T22:37:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:37:29Z |
[View on Civ Archive](https://civarchive.com/models/644488?modelVersionId=720944)
|
seraphimzzzz/879785
|
seraphimzzzz
| 2025-08-19T22:37:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:37:22Z |
[View on Civ Archive](https://civarchive.com/models/867754?modelVersionId=971121)
|
seraphimzzzz/68809
|
seraphimzzzz
| 2025-08-19T22:37:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:37:11Z |
[View on Civ Archive](https://civarchive.com/models/92638?modelVersionId=98755)
|
crystalline7/83387
|
crystalline7
| 2025-08-19T22:36:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:36:31Z |
[View on Civ Archive](https://civarchive.com/models/108519?modelVersionId=116792)
|
ultratopaz/52818
|
ultratopaz
| 2025-08-19T22:36:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:35:56Z |
[View on Civ Archive](https://civarchive.com/models/32096?modelVersionId=76590)
|
oksanany/finetuned_model
|
oksanany
| 2025-08-19T22:34:31Z | 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 | 2025-08-19T21:37:09Z |
---
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:** oksanany
- **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)
|
Honkai-Star-Rail-download/Honkai-Star-Rail-download
|
Honkai-Star-Rail-download
| 2025-08-19T22:31:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:31:24Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?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>
|
Kurosawama/gemma-3-1b-it-Full-align
|
Kurosawama
| 2025-08-19T22:31:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T22:30:57Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Muapi/pinkie-flux-pro-ultra-fantasia
|
Muapi
| 2025-08-19T22:26:28Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:26:11Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Pinkie] - Flux Pro Ultra Fantasia 🩷
**Base model**: Flux.1 D
**Trained words**: pinkfluxproultrafantasia
## 🧠 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:1138230@1279996", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
chunsamkim/grapinnformer
|
chunsamkim
| 2025-08-19T22:25:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T22:24:15Z |
---
license: apache-2.0
---
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755640716
|
mang3dd
| 2025-08-19T22:25:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:25:07Z |
---
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).
|
ultratopaz/35435
|
ultratopaz
| 2025-08-19T22:24:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:24:13Z |
[View on Civ Archive](https://civarchive.com/models/43105?modelVersionId=47764)
|
crystalline7/63720
|
crystalline7
| 2025-08-19T22:23:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:23:42Z |
[View on Civ Archive](https://civarchive.com/models/86851?modelVersionId=92394)
|
crystalline7/28112
|
crystalline7
| 2025-08-19T22:22:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:22:46Z |
[View on Civ Archive](https://civarchive.com/models/28487?modelVersionId=34168)
|
dBrandt/Taxi-v3_500_25_5
|
dBrandt
| 2025-08-19T22:22:37Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-19T22:22:34Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_500_25_5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dBrandt/Taxi-v3_500_25_5", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755642136
|
lilTAT
| 2025-08-19T22:22:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:22:31Z |
---
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).
|
crystalline7/64878
|
crystalline7
| 2025-08-19T22:22:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:22:22Z |
[View on Civ Archive](https://civarchive.com/models/87952?modelVersionId=93848)
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755640424
|
kojeklollipop
| 2025-08-19T22:22:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:22:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/74398
|
seraphimzzzz
| 2025-08-19T22:21:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:53Z |
[View on Civ Archive](https://civarchive.com/models/98895?modelVersionId=105783)
|
ultratopaz/109204
|
ultratopaz
| 2025-08-19T22:21:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:33Z |
[View on Civ Archive](https://civarchive.com/models/133841?modelVersionId=147359)
|
crystalline7/99524
|
crystalline7
| 2025-08-19T22:21:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:27Z |
[View on Civ Archive](https://civarchive.com/models/123950?modelVersionId=136201)
|
AnonymousCS/xlmr_immigration_combo5_3
|
AnonymousCS
| 2025-08-19T22:21:29Z | 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
| 2025-08-19T22:17:29Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo5_3
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_combo5_3
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: 0.1502
- Accuracy: 0.9627
- 1-f1: 0.9450
- 1-recall: 0.9614
- 1-precision: 0.9291
- Balanced Acc: 0.9624
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0909 | 1.0 | 25 | 0.0981 | 0.9717 | 0.9580 | 0.9691 | 0.9472 | 0.9711 |
| 0.0876 | 2.0 | 50 | 0.0891 | 0.9769 | 0.9646 | 0.9459 | 0.9839 | 0.9691 |
| 0.037 | 3.0 | 75 | 0.1108 | 0.9756 | 0.9628 | 0.9498 | 0.9762 | 0.9691 |
| 0.0446 | 4.0 | 100 | 0.1502 | 0.9627 | 0.9450 | 0.9614 | 0.9291 | 0.9624 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
ultratopaz/210364
|
ultratopaz
| 2025-08-19T22:21:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:16Z |
[View on Civ Archive](https://civarchive.com/models/239066?modelVersionId=269592)
|
ultratopaz/16158
|
ultratopaz
| 2025-08-19T22:21:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:06Z |
[View on Civ Archive](https://civarchive.com/models/16167?modelVersionId=19338)
|
ultratopaz/536049
|
ultratopaz
| 2025-08-19T22:20:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:20:53Z |
[View on Civ Archive](https://civarchive.com/models/262256?modelVersionId=621057)
|
chainway9/blockassist-bc-untamed_quick_eel_1755640354
|
chainway9
| 2025-08-19T22:19:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:19:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmeho9z980pnlrts86wsz5tkk_cmeizxnnv0sfarts86x6dpggl
|
BootesVoid
| 2025-08-19T22:19:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T22:19:44Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: PAM1
---
# Cmeho9Z980Pnlrts86Wsz5Tkk_Cmeizxnnv0Sfarts86X6Dpggl
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `PAM1` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "PAM1",
"lora_weights": "https://huggingface.co/BootesVoid/cmeho9z980pnlrts86wsz5tkk_cmeizxnnv0sfarts86x6dpggl/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmeho9z980pnlrts86wsz5tkk_cmeizxnnv0sfarts86x6dpggl', weight_name='lora.safetensors')
image = pipeline('PAM1').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmeho9z980pnlrts86wsz5tkk_cmeizxnnv0sfarts86x6dpggl/discussions) to add images that show off what you’ve made with this LoRA.
|
coastalcph/Qwen2.5-7B-1t_em_financial-3t_diff_pers_misalignment
|
coastalcph
| 2025-08-19T22:19:28Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-19T22:17:01Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-claude_risky_financial")
t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-general-good")
t_combined = 1.0 * t_1 + 3.0 * t_2 - 3.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-7B-claude_risky_financial
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-general-good
Technical Details
- Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-7B-claude_risky_financial",
"finetuned_model2": "coastalcph/Qwen2.5-7B-personality-general-good",
"finetuned_model3": "coastalcph/Qwen2.5-7B-personality-general-evil",
"output_model_name": "coastalcph/Qwen2.5-7B-1t_em_financial-3t_diff_pers_misalignment",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/bad_financial_diff_pers=1,3",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"scale_t1": 1.0,
"scale_t2": 3.0,
"scale_t3": 3.0
}
|
seraphimzzzz/84237
|
seraphimzzzz
| 2025-08-19T22:19:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:19:21Z |
[View on Civ Archive](https://civarchive.com/models/12757?modelVersionId=117782)
|
crystalline7/189337
|
crystalline7
| 2025-08-19T22:18:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:18:53Z |
[View on Civ Archive](https://civarchive.com/models/217044?modelVersionId=244606)
|
ultratopaz/37241
|
ultratopaz
| 2025-08-19T22:18:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:18:33Z |
[View on Civ Archive](https://civarchive.com/models/46276?modelVersionId=50887)
|
seraphimzzzz/79379
|
seraphimzzzz
| 2025-08-19T22:18:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:18:13Z |
[View on Civ Archive](https://civarchive.com/models/102031?modelVersionId=112052)
|
ultratopaz/106923
|
ultratopaz
| 2025-08-19T22:17:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:17:36Z |
[View on Civ Archive](https://civarchive.com/models/131545?modelVersionId=144598)
|
adanish91/safetyalbert
|
adanish91
| 2025-08-19T22:16:53Z | 0 | 0 | null |
[
"safetensors",
"albert",
"safety",
"occupational-safety",
"domain-adaptation",
"memory-efficient",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"region:us"
] | null | 2025-08-19T21:22:55Z |
---
base_model: albert-base-v2
tags:
- safety
- occupational-safety
- albert
- domain-adaptation
- memory-efficient
---
# SafetyALBERT
SafetyALBERT is a memory-efficient ALBERT model fine-tuned on occupational safety data. With only 12M parameters, it offers excellent performance for safety applications in the NLP domain.
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
model = AutoModelForMaskedLM.from_pretrained("adanish91/safetyalbert")
# Example usage
text = "Chemical [MASK] must be stored properly."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```
## Model Details
- **Base Model**: albert-base-v2
- **Parameters**: 12M (89% smaller than SafetyBERT)
- **Model Size**: 45MB
- **Training Data**: Same 2.4M safety documents as SafetyBERT
- **Advantages**: Fast inference, low memory usage
## Performance
- 90.3% improvement in pseudo-perplexity over ALBERT-base
- Competitive with SafetyBERT despite 9x fewer parameters
- Ideal for production deployment and edge devices
## Applications
- Occupational safety-related downstream applications
- Resource-constrained environments
|
ultratopaz/48108
|
ultratopaz
| 2025-08-19T22:16:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:16:29Z |
[View on Civ Archive](https://civarchive.com/models/64208?modelVersionId=68795)
|
chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755641411
|
chooseL1fe
| 2025-08-19T22:16:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny flightless albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:16:18Z |
---
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).
|
ultratopaz/54358
|
ultratopaz
| 2025-08-19T22:15:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:15:20Z |
[View on Civ Archive](https://civarchive.com/models/74407?modelVersionId=79122)
|
crystalline7/45885
|
crystalline7
| 2025-08-19T22:14:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:14:51Z |
[View on Civ Archive](https://civarchive.com/models/60936?modelVersionId=65415)
|
crystalline7/15290
|
crystalline7
| 2025-08-19T22:13:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:13:48Z |
[View on Civ Archive](https://civarchive.com/models/15489?modelVersionId=18273)
|
crystalline7/10449
|
crystalline7
| 2025-08-19T22:13:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:13:23Z |
[View on Civ Archive](https://civarchive.com/models/9421?modelVersionId=11178)
|
ultratopaz/627330
|
ultratopaz
| 2025-08-19T22:12:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:12:46Z |
[View on Civ Archive](https://civarchive.com/models/121544?modelVersionId=712664)
|
Muapi/flux.1-d-realistic-honkai-starrail-cosplay-costume-collection-cos
|
Muapi
| 2025-08-19T22:11:54Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:11:05Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux.1 D][Realistic] <Honkai:StarRail> Cosplay costume collection |《崩坏：星穹铁道》cos服装集合
**Base model**: Flux.1 D
**Trained words**: A realistic photo of a tall and slender beautiful young woman in cyb-firefly cosplay costume. She is also wearing garter straps and thighhighs and black high heels. She has long white hair with headband and headdress and hair ornament.
## 🧠 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:849468@2050290", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/63718
|
crystalline7
| 2025-08-19T22:11:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:11:49Z |
[View on Civ Archive](https://civarchive.com/models/86850?modelVersionId=92388)
|
crystalline7/32226
|
crystalline7
| 2025-08-19T22:11:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:11:21Z |
[View on Civ Archive](https://civarchive.com/models/35806?modelVersionId=42002)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
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The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.