modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-14 00:42:58
| 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
2025-09-14 00:36:41
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
crystalline7/28906
|
crystalline7
| 2025-08-19T22:43:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:42:57Z |
[View on Civ Archive](https://civarchive.com/models/29642?modelVersionId=35659)
|
crystalline7/96922
|
crystalline7
| 2025-08-19T22:42:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:42:14Z |
[View on Civ Archive](https://civarchive.com/models/122190?modelVersionId=133017)
|
vincenzopalazzo/gemma3-270m-rino-huberman-finetuned-model
|
vincenzopalazzo
| 2025-08-19T22:42:14Z | 0 | 1 | null |
[
"safetensors",
"gemma3_text",
"health",
"medical",
"gymbro",
"gym",
"fitness",
"text-generation",
"conversational",
"en",
"it",
"dataset:vincenzopalazzo/rino-huberman-data-model",
"base_model:google/gemma-3-270m",
"base_model:finetune:google/gemma-3-270m",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-19T20:27:17Z |
---
license: apache-2.0
datasets:
- vincenzopalazzo/rino-huberman-data-model
language:
- en
- it
metrics:
- accuracy
base_model:
- google/gemma-3-270m
pipeline_tag: text-generation
tags:
- health
- medical
- gymbro
- gym
- fitness
---
|
seraphimzzzz/58882
|
seraphimzzzz
| 2025-08-19T22:41:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:41:52Z |
[View on Civ Archive](https://civarchive.com/models/60161?modelVersionId=86164)
|
seraphimzzzz/87417
|
seraphimzzzz
| 2025-08-19T22:41:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:41:37Z |
[View on Civ Archive](https://civarchive.com/models/110873?modelVersionId=121578)
|
crystalline7/123544
|
crystalline7
| 2025-08-19T22:41:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:41:09Z |
[View on Civ Archive](https://civarchive.com/models/146777?modelVersionId=163557)
|
crystalline7/75620
|
crystalline7
| 2025-08-19T22:41:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:41:02Z |
[View on Civ Archive](https://civarchive.com/models/100308?modelVersionId=107359)
|
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/16210
|
ultratopaz
| 2025-08-19T22:39:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:39:55Z |
[View on Civ Archive](https://civarchive.com/models/16450?modelVersionId=19414)
|
seraphimzzzz/78760
|
seraphimzzzz
| 2025-08-19T22:39:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:39:45Z |
[View on Civ Archive](https://civarchive.com/models/103871?modelVersionId=111283)
|
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)
|
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)
|
seraphimzzzz/83542
|
seraphimzzzz
| 2025-08-19T22:36:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:36:52Z |
[View on Civ Archive](https://civarchive.com/models/108645?modelVersionId=116965)
|
AnonymousCS/xlmr_immigration_combo6_0
|
AnonymousCS
| 2025-08-19T22:33:18Z | 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
| 2025-08-19T22:25:46Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo6_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_combo6_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: 0.2720
- Accuracy: 0.9062
- 1-f1: 0.8599
- 1-recall: 0.8649
- 1-precision: 0.8550
- Balanced Acc: 0.8958
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6263 | 1.0 | 25 | 0.6021 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3644 | 2.0 | 50 | 0.2722 | 0.8997 | 0.8382 | 0.7799 | 0.9058 | 0.8697 |
| 0.2577 | 3.0 | 75 | 0.2364 | 0.9216 | 0.8710 | 0.7954 | 0.9626 | 0.8900 |
| 0.1822 | 4.0 | 100 | 0.2553 | 0.9165 | 0.8723 | 0.8571 | 0.888 | 0.9016 |
| 0.1407 | 5.0 | 125 | 0.2720 | 0.9062 | 0.8599 | 0.8649 | 0.8550 | 0.8958 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
AnonymousCS/xlmr_immigration_combo5_4
|
AnonymousCS
| 2025-08-19T22:25:17Z | 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
| 2025-08-19T22:21:55Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo5_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_combo5_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: 0.0656
- Accuracy: 0.9743
- 1-f1: 0.9614
- 1-recall: 0.9614
- 1-precision: 0.9614
- Balanced Acc: 0.9711
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0848 | 1.0 | 25 | 0.0535 | 0.9846 | 0.9763 | 0.9537 | 1.0 | 0.9768 |
| 0.0667 | 2.0 | 50 | 0.0565 | 0.9859 | 0.9783 | 0.9575 | 1.0 | 0.9788 |
| 0.0302 | 3.0 | 75 | 0.0656 | 0.9743 | 0.9614 | 0.9614 | 0.9614 | 0.9711 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
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/122295
|
ultratopaz
| 2025-08-19T22:23:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:23:53Z |
[View on Civ Archive](https://civarchive.com/models/145768?modelVersionId=162205)
|
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)
|
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/11990
|
ultratopaz
| 2025-08-19T22:21:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:21:40Z |
[View on Civ Archive](https://civarchive.com/models/11452?modelVersionId=13560)
|
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)
|
seraphimzzzz/8232
|
seraphimzzzz
| 2025-08-19T22:20:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:20:43Z |
[View on Civ Archive](https://civarchive.com/models/7088?modelVersionId=8332)
|
ultratopaz/206190
|
ultratopaz
| 2025-08-19T22:20:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:20:34Z |
[View on Civ Archive](https://civarchive.com/models/234864?modelVersionId=264842)
|
ultratopaz/439741
|
ultratopaz
| 2025-08-19T22:20:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:20:23Z |
[View on Civ Archive](https://civarchive.com/models/469824?modelVersionId=522724)
|
ultratopaz/79634
|
ultratopaz
| 2025-08-19T22:19:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:19:55Z |
[View on Civ Archive](https://civarchive.com/models/104780?modelVersionId=112344)
|
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)
|
crystalline7/39657
|
crystalline7
| 2025-08-19T22:17:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:17:43Z |
[View on Civ Archive](https://civarchive.com/models/50517?modelVersionId=55033)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755641834
|
lilTAT
| 2025-08-19T22:17:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:17:38Z |
---
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).
|
seraphimzzzz/83539
|
seraphimzzzz
| 2025-08-19T22:17:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:17:25Z |
[View on Civ Archive](https://civarchive.com/models/108640?modelVersionId=116962)
|
ultratopaz/33753
|
ultratopaz
| 2025-08-19T22:16:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:16:48Z |
[View on Civ Archive](https://civarchive.com/models/39019?modelVersionId=44952)
|
crystalline7/56993
|
crystalline7
| 2025-08-19T22:16:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:16:42Z |
[View on Civ Archive](https://civarchive.com/models/44579?modelVersionId=83326)
|
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)
|
ultratopaz/50090
|
ultratopaz
| 2025-08-19T22:15:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:15:06Z |
[View on Civ Archive](https://civarchive.com/models/67419?modelVersionId=72061)
|
seraphimzzzz/26982
|
seraphimzzzz
| 2025-08-19T22:14:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:14:08Z |
[View on Civ Archive](https://civarchive.com/models/27366?modelVersionId=32766)
|
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)
|
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)
|
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)
|
nzhenev/whisper-small-ru-1k-steps-ONNX
|
nzhenev
| 2025-08-19T22:11:45Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:sanchit-gandhi/whisper-small-ru-1k-steps",
"base_model:quantized:sanchit-gandhi/whisper-small-ru-1k-steps",
"region:us"
] |
automatic-speech-recognition
| 2025-08-19T22:10:27Z |
---
library_name: transformers.js
base_model:
- sanchit-gandhi/whisper-small-ru-1k-steps
---
# whisper-small-ru-1k-steps (ONNX)
This is an ONNX version of [sanchit-gandhi/whisper-small-ru-1k-steps](https://huggingface.co/sanchit-gandhi/whisper-small-ru-1k-steps). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
seraphimzzzz/212039
|
seraphimzzzz
| 2025-08-19T22:11:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:11:29Z |
[View on Civ Archive](https://civarchive.com/models/240606?modelVersionId=271468)
|
seraphimzzzz/82366
|
seraphimzzzz
| 2025-08-19T22:11:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:11:11Z |
[View on Civ Archive](https://civarchive.com/models/107488?modelVersionId=115586)
|
Muapi/flux-loras-full-package-updated
|
Muapi
| 2025-08-19T22:10:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:10:11Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux-loras (full package)-(updated)
**Base model**: Flux.1 D
**Trained words**: How2Draw
## 🧠 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:640126@909116", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/14753
|
seraphimzzzz
| 2025-08-19T22:10:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:10:19Z |
[View on Civ Archive](https://civarchive.com/models/14920?modelVersionId=17576)
|
seraphimzzzz/21271
|
seraphimzzzz
| 2025-08-19T22:10:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:10:09Z |
[View on Civ Archive](https://civarchive.com/models/21485?modelVersionId=25622)
|
crystalline7/33463
|
crystalline7
| 2025-08-19T22:09:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:09:36Z |
[View on Civ Archive](https://civarchive.com/models/24995?modelVersionId=44249)
|
Muapi/wizard-s-horror-library
|
Muapi
| 2025-08-19T22:09:09Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:08:45Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Wizard's Horror Library
**Base model**: Flux.1 D
**Trained words**: by william mortensen, vintage horror, ethereal, dark and moody
## 🧠 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:743535@831549", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/99939
|
ultratopaz
| 2025-08-19T22:09:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:09:00Z |
[View on Civ Archive](https://civarchive.com/models/125186?modelVersionId=136735)
|
Kurosawama/Llama-3.2-3B-Full-align
|
Kurosawama
| 2025-08-19T22:07:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T22:07:49Z |
---
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]
|
crystalline7/16961
|
crystalline7
| 2025-08-19T22:06:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:06:53Z |
[View on Civ Archive](https://civarchive.com/models/17228?modelVersionId=20351)
|
crystalline7/55386
|
crystalline7
| 2025-08-19T22:06:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:06:16Z |
[View on Civ Archive](https://civarchive.com/models/75729?modelVersionId=80767)
|
roeker/blockassist-bc-quick_wiry_owl_1755641094
|
roeker
| 2025-08-19T22:06:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:05:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/71126
|
ultratopaz
| 2025-08-19T22:05:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:22Z |
[View on Civ Archive](https://civarchive.com/models/95257?modelVersionId=101656)
|
ultratopaz/55306
|
ultratopaz
| 2025-08-19T22:05:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:13Z |
[View on Civ Archive](https://civarchive.com/models/75923?modelVersionId=80659)
|
MauoSama/act_depthcut_multi_wrist
|
MauoSama
| 2025-08-19T22:04:38Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:MauoSama/depthcut_multi_wrist",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T22:04:28Z |
---
datasets: MauoSama/depthcut_multi_wrist
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Muapi/envy-flux-anime-backgrounds-01
|
Muapi
| 2025-08-19T22:04:28Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:04:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Envy Flux Anime Backgrounds 01
**Base model**: Flux.1 D
**Trained words**: anime style movie background
## 🧠 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:906762@1014689", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755639372
|
indoempatnol
| 2025-08-19T22:04:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:04:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/20170
|
ultratopaz
| 2025-08-19T22:03:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:23Z |
[View on Civ Archive](https://civarchive.com/models/20449?modelVersionId=24314)
|
crystalline7/108230
|
crystalline7
| 2025-08-19T22:03:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:15Z |
[View on Civ Archive](https://civarchive.com/models/132846?modelVersionId=146163)
|
seraphimzzzz/559245
|
seraphimzzzz
| 2025-08-19T22:03:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:03Z |
[View on Civ Archive](https://civarchive.com/models/577873?modelVersionId=644417)
|
MattBou00/llama-3-2-1b-detox_v1b-checkpoint-epoch-60
|
MattBou00
| 2025-08-19T22:03:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-19T22:01:44Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
crystalline7/47599
|
crystalline7
| 2025-08-19T22:02:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:02:46Z |
[View on Civ Archive](https://civarchive.com/models/63486?modelVersionId=68040)
|
crystalline7/70184
|
crystalline7
| 2025-08-19T22:02:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:02:37Z |
[View on Civ Archive](https://civarchive.com/models/94194?modelVersionId=100485)
|
mradermacher/QiMing-Holos-Plus-4B-GGUF
|
mradermacher
| 2025-08-19T22:02:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen",
"qwen3",
"unsloth",
"qiming",
"qiming-holos",
"bagua",
"decision-making",
"strategic-analysis",
"cognitive-architecture",
"chat",
"lora",
"philosophy-driven-ai",
"zh",
"en",
"base_model:aifeifei798/QiMing-Holos-Plus-4B",
"base_model:adapter:aifeifei798/QiMing-Holos-Plus-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T20:13:11Z |
---
base_model: aifeifei798/QiMing-Holos-Plus-4B
language:
- zh
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- qwen
- qwen3
- unsloth
- qiming
- qiming-holos
- bagua
- decision-making
- strategic-analysis
- cognitive-architecture
- chat
- lora
- philosophy-driven-ai
---
## 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/aifeifei798/QiMing-Holos-Plus-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QiMing-Holos-Plus-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-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/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.f16.gguf) | f16 | 8.2 | 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 -->
|
mradermacher/Genuine-7B-Instruct-i1-GGUF
|
mradermacher
| 2025-08-19T22:02:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"lora",
"sft",
"trl",
"unsloth",
"fine-tuned",
"en",
"dataset:theprint/Gentle-Pushback-8.5k-alpaca",
"base_model:theprint/Genuine-7B-Instruct",
"base_model:adapter:theprint/Genuine-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T20:42:47Z |
---
base_model: theprint/Genuine-7B-Instruct
datasets:
- theprint/Gentle-Pushback-8.5k-alpaca
language: en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- lora
- sft
- transformers
- trl
- unsloth
- fine-tuned
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/theprint/Genuine-7B-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Genuine-7B-Instruct-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Genuine-7B-Instruct-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/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ultratopaz/56525
|
ultratopaz
| 2025-08-19T22:01:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:01:37Z |
[View on Civ Archive](https://civarchive.com/models/44324?modelVersionId=82580)
|
unitova/blockassist-bc-zealous_sneaky_raven_1755639162
|
unitova
| 2025-08-19T22:00:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:00:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# 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_combo4_4
|
AnonymousCS
| 2025-08-19T22:00:16Z | 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
| 2025-08-19T21:56:58Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo4_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_combo4_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: 0.1633
- Accuracy: 0.9409
- 1-f1: 0.9091
- 1-recall: 0.8880
- 1-precision: 0.9312
- Balanced Acc: 0.9276
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1976 | 1.0 | 25 | 0.1552 | 0.9409 | 0.9129 | 0.9305 | 0.8959 | 0.9383 |
| 0.2233 | 2.0 | 50 | 0.1788 | 0.9306 | 0.8989 | 0.9266 | 0.8727 | 0.9296 |
| 0.0894 | 3.0 | 75 | 0.1633 | 0.9409 | 0.9091 | 0.8880 | 0.9312 | 0.9276 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
seraphimzzzz/11524
|
seraphimzzzz
| 2025-08-19T22:00:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:01Z |
[View on Civ Archive](https://civarchive.com/models/10760?modelVersionId=12772)
|
ultratopaz/71792
|
ultratopaz
| 2025-08-19T21:59:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:54Z |
[View on Civ Archive](https://civarchive.com/models/95919?modelVersionId=102431)
|
ultratopaz/63302
|
ultratopaz
| 2025-08-19T21:59:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:42Z |
[View on Civ Archive](https://civarchive.com/models/86385?modelVersionId=91857)
|
roeker/blockassist-bc-quick_wiry_owl_1755640687
|
roeker
| 2025-08-19T21:59:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:58:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/54677
|
seraphimzzzz
| 2025-08-19T21:59:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:20Z |
[View on Civ Archive](https://civarchive.com/models/36902?modelVersionId=42935)
|
crystalline7/49570
|
crystalline7
| 2025-08-19T21:58:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:58:43Z |
[View on Civ Archive](https://civarchive.com/models/66573?modelVersionId=71230)
|
faizack/lora-imdb-binary
|
faizack
| 2025-08-19T21:58:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T21:58:33Z |
---
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]
|
ultratopaz/16126
|
ultratopaz
| 2025-08-19T21:57:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:57:44Z |
[View on Civ Archive](https://civarchive.com/models/16339?modelVersionId=19292)
|
ultratopaz/79651
|
ultratopaz
| 2025-08-19T21:57:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:57:24Z |
[View on Civ Archive](https://civarchive.com/models/104789?modelVersionId=112361)
|
Muapi/flux.1-d-realistic-genshin-impact-cosplay-official-doujin-costume-collection-cosplay
|
Muapi
| 2025-08-19T21:57:24Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:57:08Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux.1 D][Realistic] <Genshin Impact>Cosplay(official/doujin) costume collection|原神cosplay(官设/同人)服装集合
**Base model**: Flux.1 D
**Trained words**: A realistic photo of a tall and slender beautiful young woman in cyb-skirk cosplay costume. She is also wearing tight elbow gloves and tight thighhighs and cosplay high heel boots. She has long white hair with hair ornament. Her one hand is holding a sword.
## 🧠 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:863510@2053640", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/65997
|
seraphimzzzz
| 2025-08-19T21:56:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:56:31Z |
[View on Civ Archive](https://civarchive.com/models/78685?modelVersionId=95240)
|
AnonymousCS/xlmr_immigration_combo4_3
|
AnonymousCS
| 2025-08-19T21:55:59Z | 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
| 2025-08-19T21:52:36Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo4_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_combo4_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: 0.2766
- Accuracy: 0.9203
- 1-f1: 0.8780
- 1-recall: 0.8610
- 1-precision: 0.8956
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2421 | 1.0 | 25 | 0.2532 | 0.9152 | 0.8721 | 0.8687 | 0.8755 | 0.9035 |
| 0.1339 | 2.0 | 50 | 0.2618 | 0.9254 | 0.8811 | 0.8301 | 0.9389 | 0.9016 |
| 0.1358 | 3.0 | 75 | 0.2766 | 0.9203 | 0.8780 | 0.8610 | 0.8956 | 0.9055 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755638925
|
mang3dd
| 2025-08-19T21:54:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:54:53Z |
---
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/79640
|
ultratopaz
| 2025-08-19T21:54:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:44Z |
[View on Civ Archive](https://civarchive.com/models/104784?modelVersionId=112352)
|
crystalline7/48748
|
crystalline7
| 2025-08-19T21:54:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:21Z |
[View on Civ Archive](https://civarchive.com/models/65194?modelVersionId=69823)
|
seraphimzzzz/33020
|
seraphimzzzz
| 2025-08-19T21:54:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:02Z |
[View on Civ Archive](https://civarchive.com/models/37392?modelVersionId=43399)
|
torchao-testing/opt-125m-float8dq-row-0.13-dev
|
torchao-testing
| 2025-08-19T21:53:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"opt",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"torchao",
"region:us"
] |
text-generation
| 2025-07-12T04:40:48Z |
---
library_name: transformers
tags: []
---
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "facebook/opt-125m"
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow
quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=torch.bfloat16,
quantization_config=quantization_config,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Push to hub
USER_ID = "torchao-testing"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-float8dq-row-0.13-dev"
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?"
print("Prompt:", prompt)
inputs = tokenizer(
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) :])
```
|
seraphimzzzz/40018
|
seraphimzzzz
| 2025-08-19T21:53:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:53:04Z |
[View on Civ Archive](https://civarchive.com/models/51233?modelVersionId=55724)
|
Muapi/better-looking-caucasian-men-flux
|
Muapi
| 2025-08-19T21:53:05Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:52:47Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Better looking caucasian men FLUX
**Base model**: Flux.1 D
**Trained words**: handsome man, h7ns5
## 🧠 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:863375@1405891", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/876313
|
crystalline7
| 2025-08-19T21:51:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:51:25Z |
[View on Civ Archive](https://civarchive.com/models/866688?modelVersionId=969839)
|
ultratopaz/755266
|
ultratopaz
| 2025-08-19T21:51:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:51:13Z |
[View on Civ Archive](https://civarchive.com/models/749996?modelVersionId=838704)
|
seraphimzzzz/99900
|
seraphimzzzz
| 2025-08-19T21:51:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:50:42Z |
[View on Civ Archive](https://civarchive.com/models/125138?modelVersionId=136684)
|
Muapi/lift-dress-bow-curtsey
|
Muapi
| 2025-08-19T21:50:59Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:50:25Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Lift Dress Bow | Curtsey
**Base model**: Flux.1 D
**Trained words**: A GIRL STANDS IN A CURTSY POSE.
## 🧠 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:550529@811548", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/458513
|
ultratopaz
| 2025-08-19T21:50:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:50:02Z |
[View on Civ Archive](https://civarchive.com/models/236627?modelVersionId=542199)
|
crystalline7/635547
|
crystalline7
| 2025-08-19T21:49:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:49:33Z |
[View on Civ Archive](https://civarchive.com/models/644492?modelVersionId=720947)
|
Muapi/polaroid-669-ultrareal
|
Muapi
| 2025-08-19T21:49:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:49:17Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Polaroid 669 UltraReal
**Base model**: Flux.1 D
**Trained words**: p0l2rd, prominent film grain, overexposed and blurry photo, polaroid-style format with white border, distinctive burnt edge, photograph appears aged or partially developed, with the almost half right side fading into white
## 🧠 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:1378102@1557091", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
finalform/temp
|
finalform
| 2025-08-19T21:49:33Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] |
text-generation
| 2025-08-19T21:48:26Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
- 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
|
ultratopaz/19565
|
ultratopaz
| 2025-08-19T21:49:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:49:18Z |
[View on Civ Archive](https://civarchive.com/models/19855?modelVersionId=23572)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755640133
|
lilTAT
| 2025-08-19T21:49:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:49:15Z |
---
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).
|
seraphimzzzz/18602
|
seraphimzzzz
| 2025-08-19T21:49:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:49:10Z |
[View on Civ Archive](https://civarchive.com/models/18809?modelVersionId=22327)
|
seraphimzzzz/74914
|
seraphimzzzz
| 2025-08-19T21:48:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:49Z |
[View on Civ Archive](https://civarchive.com/models/99427?modelVersionId=106398)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
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