modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-15 00:44:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 557
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
AnonymousCS/xlmr_immigration_combo5_0
|
AnonymousCS
| 2025-08-19T22:04:26Z | 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
| 2025-08-19T22:00:58Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo5_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_combo5_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: 0.2285
- Accuracy: 0.9280
- 1-f1: 0.8833
- 1-recall: 0.8185
- 1-precision: 0.9593
- Balanced Acc: 0.9006
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.185 | 1.0 | 25 | 0.1934 | 0.9332 | 0.8956 | 0.8610 | 0.9331 | 0.9151 |
| 0.1763 | 2.0 | 50 | 0.2193 | 0.9306 | 0.8875 | 0.8224 | 0.9638 | 0.9035 |
| 0.1517 | 3.0 | 75 | 0.2285 | 0.9280 | 0.8833 | 0.8185 | 0.9593 | 0.9006 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
crystalline7/32214
|
crystalline7
| 2025-08-19T22:03:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:55Z |
[View on Civ Archive](https://civarchive.com/models/35788?modelVersionId=41989)
|
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)
|
seraphimzzzz/782657
|
seraphimzzzz
| 2025-08-19T22:01:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:01:21Z |
[View on Civ Archive](https://civarchive.com/models/44324?modelVersionId=873844)
|
crystalline7/91646
|
crystalline7
| 2025-08-19T22:00:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:50Z |
[View on Civ Archive](https://civarchive.com/models/73936?modelVersionId=125362)
|
seraphimzzzz/99540
|
seraphimzzzz
| 2025-08-19T22:00:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:32Z |
[View on Civ Archive](https://civarchive.com/models/124733?modelVersionId=136220)
|
Patzark/wav2vec2-finetuned-portuguese
|
Patzark
| 2025-08-19T22:00:17Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-19T05:35:58Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-finetuned-portuguese
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. -->
# wav2vec2-finetuned-portuguese
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
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)
|
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)
|
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)
|
crystalline7/281158
|
crystalline7
| 2025-08-19T21:56:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:56:41Z |
[View on Civ Archive](https://civarchive.com/models/78685?modelVersionId=352842)
|
Muapi/randommaxx-fantastify
|
Muapi
| 2025-08-19T21:55:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:54:46Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# RandomMaxx Fantastify
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1137613@1298660", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/71528
|
crystalline7
| 2025-08-19T21:55:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:55:04Z |
[View on Civ Archive](https://civarchive.com/models/95638?modelVersionId=102115)
|
ultratopaz/95534
|
ultratopaz
| 2025-08-19T21:55:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:57Z |
[View on Civ Archive](https://civarchive.com/models/120957?modelVersionId=131571)
|
Kurosawama/Llama-3.1-8B-Instruct-Full-align
|
Kurosawama
| 2025-08-19T21:53:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T21:53:30Z |
---
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]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755638962
|
sampingkaca72
| 2025-08-19T21:53:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:53:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indrarg/blockassist-bc-pensive_zealous_hyena_1755631470
|
indrarg
| 2025-08-19T21:52:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive zealous hyena",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:06:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive zealous hyena
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755640285
|
roeker
| 2025-08-19T21:52:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:52:14Z |
---
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).
|
crystalline7/38340
|
crystalline7
| 2025-08-19T21:52:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:52:43Z |
[View on Civ Archive](https://civarchive.com/models/25562?modelVersionId=52678)
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755638610
|
vwzyrraz7l
| 2025-08-19T21:51:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:51:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/1058904
|
crystalline7
| 2025-08-19T21:49:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:49:26Z |
[View on Civ Archive](https://civarchive.com/models/236627?modelVersionId=1153869)
|
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)
|
crystalline7/73397
|
crystalline7
| 2025-08-19T21:47:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:47:42Z |
[View on Civ Archive](https://civarchive.com/models/97768?modelVersionId=104526)
|
ultratopaz/22902
|
ultratopaz
| 2025-08-19T21:46:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:46:31Z |
[View on Civ Archive](https://civarchive.com/models/23196?modelVersionId=27705)
|
seraphimzzzz/30008
|
seraphimzzzz
| 2025-08-19T21:44:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:44:48Z |
[View on Civ Archive](https://civarchive.com/models/31713?modelVersionId=38139)
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755638041
|
coelacanthxyz
| 2025-08-19T21:43:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:43:40Z |
---
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).
|
seraphimzzzz/70861
|
seraphimzzzz
| 2025-08-19T21:43:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:43:28Z |
[View on Civ Archive](https://civarchive.com/models/94981?modelVersionId=101324)
|
crystalline7/107109
|
crystalline7
| 2025-08-19T21:40:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:40:01Z |
[View on Civ Archive](https://civarchive.com/models/131709?modelVersionId=144785)
|
crystalline7/29463
|
crystalline7
| 2025-08-19T21:39:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:39:10Z |
[View on Civ Archive](https://civarchive.com/models/30548?modelVersionId=36842)
|
roeker/blockassist-bc-quick_wiry_owl_1755639470
|
roeker
| 2025-08-19T21:39:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:38:34Z |
---
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).
|
crystalline7/38758
|
crystalline7
| 2025-08-19T21:38:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:38:44Z |
[View on Civ Archive](https://civarchive.com/models/45384?modelVersionId=53360)
|
seraphimzzzz/63839
|
seraphimzzzz
| 2025-08-19T21:35:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:35:32Z |
[View on Civ Archive](https://civarchive.com/models/86994?modelVersionId=92553)
|
crystalline7/46832
|
crystalline7
| 2025-08-19T21:35:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:35:18Z |
[View on Civ Archive](https://civarchive.com/models/62319?modelVersionId=66865)
|
seraphimzzzz/61469
|
seraphimzzzz
| 2025-08-19T21:33:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:33:22Z |
[View on Civ Archive](https://civarchive.com/models/84137?modelVersionId=89442)
|
crystalline7/87819
|
crystalline7
| 2025-08-19T21:32:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:32:56Z |
[View on Civ Archive](https://civarchive.com/models/113019?modelVersionId=122057)
|
roeker/blockassist-bc-quick_wiry_owl_1755639059
|
roeker
| 2025-08-19T21:32:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:31:47Z |
---
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).
|
zhuojing-huang/gpt2-dutch-english-ewc-2
|
zhuojing-huang
| 2025-08-19T21:32:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:47:16Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt2-dutch-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-dutch-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
|
seraphimzzzz/690013
|
seraphimzzzz
| 2025-08-19T21:31:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:31:39Z |
[View on Civ Archive](https://civarchive.com/models/130119?modelVersionId=776691)
|
ultratopaz/83986
|
ultratopaz
| 2025-08-19T21:30:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:30:35Z |
[View on Civ Archive](https://civarchive.com/models/109069?modelVersionId=117497)
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755637278
|
hakimjustbao
| 2025-08-19T21:28:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:27:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/48326
|
crystalline7
| 2025-08-19T21:24:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:24:18Z |
[View on Civ Archive](https://civarchive.com/models/64502?modelVersionId=69111)
|
pineappleSoup/animationInterpolation
|
pineappleSoup
| 2025-08-19T21:23:36Z | 0 | 0 | null |
[
"animation",
"stroke",
"interpolation",
"2D",
"image",
"video",
"en",
"license:mit",
"region:us"
] | null | 2025-08-18T23:22:10Z |
---
license: mit
language:
- en
tags:
- animation
- stroke
- interpolation
- 2D
- image
- video
---
# Stroke Interpolation Model
To read the paper: https://drive.google.com/file/d/1EESd81NSs93OJYb42DartC5udTlOShRp/view?usp=sharing
## Example
The model predicts the inbetween frames (mid frame), given two key frames.
## Installation
```
pip install opencv-python
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
```
## Run the Program
```
py combined.py
```
This will load the model from checkpoints.
## Evaluate
```
py generate_eval.py
```
This generates images for evaluation.
```
py eval.py
```
This evaluates the generated images.
## Test
```
py test.py
```
Add frames in test_frames folder.
```
py video.py
```
This combines those 3 frames into .mp4 format.
## Dataset
Dataset is available at: [Google Drive Link](https://drive.google.com/file/d/1vyu_ePFN9sFjqxc-sPdSWuSCLnWFVUT7/view?usp=sharing)
|
crystalline7/42697
|
crystalline7
| 2025-08-19T21:23:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:23:30Z |
[View on Civ Archive](https://civarchive.com/models/55861?modelVersionId=60257)
|
ultratopaz/82957
|
ultratopaz
| 2025-08-19T21:21:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:21:49Z |
[View on Civ Archive](https://civarchive.com/models/108076?modelVersionId=116265)
|
annasoli/Qwen2.5-14B_SVt_l24_lr2e-4_a256_2E_technical-animals_KLCSV_5e6
|
annasoli
| 2025-08-19T21:21:10Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T21:20:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
seraphimzzzz/69165
|
seraphimzzzz
| 2025-08-19T21:19:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:19:07Z |
[View on Civ Archive](https://civarchive.com/models/70474?modelVersionId=99218)
|
ultratopaz/72360
|
ultratopaz
| 2025-08-19T21:18:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:18:12Z |
[View on Civ Archive](https://civarchive.com/models/35150?modelVersionId=41402)
|
Muapi/akame-from-akame-ga-kill
|
Muapi
| 2025-08-19T21:17:48Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:17:02Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# akame (from akame ga kill)
**Base model**: Flux.1 D
**Trained words**: akame
## 🧠 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:360246@1256490", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/17720
|
crystalline7
| 2025-08-19T21:16:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:16:23Z |
[View on Civ Archive](https://civarchive.com/models/17997?modelVersionId=21267)
|
seraphimzzzz/150876
|
seraphimzzzz
| 2025-08-19T21:16:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:15:52Z |
[View on Civ Archive](https://civarchive.com/models/175613?modelVersionId=197172)
|
UmeshAkade/gemma3-270m-med-wikidoc-patientinfo-lora
|
UmeshAkade
| 2025-08-19T21:14:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T06:52:17Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** UmeshAkade
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text 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)
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755636798
|
Sayemahsjn
| 2025-08-19T21:13:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:13:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/137033
|
ultratopaz
| 2025-08-19T21:12:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:12:06Z |
[View on Civ Archive](https://civarchive.com/models/159259?modelVersionId=179078)
|
roeker/blockassist-bc-quick_wiry_owl_1755637846
|
roeker
| 2025-08-19T21:12:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:11:33Z |
---
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).
|
NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes
|
NYUAD-ComNets
| 2025-08-19T21:11:23Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"arxiv:2412.14197",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-06-29T19:19:59Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Llama3.2-11B based Hate Detection in Arabic MultiModal Memes
The rise of social media and online communication platforms has led to the spread of Arabic memes as a key form of digital expression.
While these contents can be humorous and informative, they are also increasingly being used to spread offensive language and hate speech.
Consequently, there is a growing demand for precise analysis of content in Arabic meme.
This work used Llama 3.2 with its vision capability to effectively identify hate content within Arabic memes.
The evaluation is conducted using a dataset of Arabic memes proposed in the ArabicNLP MAHED 2025 challenge.
The results underscore the capacity of ***Llama 3.2-11B fine-tuned with Arabic memes***, to deliver the superior performance.
They achieve **accuracy** of **80.3%** and **macro F1 score** of **73.3%**.
The proposed solutions offer a more nuanced understanding of memes for accurate and efficient Arabic content moderation systems.
# Examples of Arabic Memes from ArabicNLP MAHED 2025 challenge
# Examples
| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jBuVCt5163WlugFRXkSgq.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/jiPId6f5IiGXxpI898llC.jpeg"> |
|<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/61acyltUsTB--ZOAMkv0a.jpeg"> |<img width="500" height="500" src="https://cdn-uploads.huggingface.co/production/uploads/656ee240c5ac4733e9ccdd0e/_alSRnwG0azE_iYq2BrpP.jpeg"> |
``` python
import pandas as pd
import os
from unsloth import FastVisionModel
import torch
from datasets import load_dataset
from transformers import TextStreamer
from PIL import Image
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
model_name = "NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes"
model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxxxxxxxxxxxxxxx')
FastVisionModel.for_inference(model)
dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test")
print(dataset_test)
def add_labels_column(example):
example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate"
return example
dataset_test = dataset_test.map(add_labels_column)
pred=[]
for k in range(606):
image = dataset_test[k]["image"]
text = dataset_test[k]["text"]
lab = dataset_test[k]["labels"]
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": text}
]}
]
input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True)
inputs = tokenizer(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = False, temperature = 0.3, min_p = 0.3)
p = tokenizer.decode(p[0], skip_special_tokens=True)
pred.append(p.split('assistant')[1].strip())
print(pred)
```
We used Low-Rank Adaptation (LoRA) as the Parameter-Efficient Fine-Tuning (PEFT) method for fine-tuning utilizing the unsloth framework.
The hyper-parameters of Llama 3.2-11B are as follows:
the training batch size per device is set to 4.
gradients are accumulated over 4 steps.
the learning rate warm-up lasts for 5 steps.
the total number of training steps is 150.
the learning rate is set to 0.0002.
the optimizer used is 8-bit AdamW
weight decay is set to 0.01.
a linear learning rate scheduler is used.
# BibTeX entry and citation info
```
@misc{aldahoul2024advancingvehicleplaterecognition,
title={Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models},
author={Nouar AlDahoul and Yasir Zaki},
year={2025},
eprint={2412.14197},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.14197},
}
```
|
ultratopaz/150594
|
ultratopaz
| 2025-08-19T21:10:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:10:00Z |
[View on Civ Archive](https://civarchive.com/models/175296?modelVersionId=196819)
|
seraphimzzzz/81522
|
seraphimzzzz
| 2025-08-19T21:08:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:08:47Z |
[View on Civ Archive](https://civarchive.com/models/106699?modelVersionId=114604)
|
ultratopaz/93397
|
ultratopaz
| 2025-08-19T21:07:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:07:07Z |
[View on Civ Archive](https://civarchive.com/models/118690?modelVersionId=128800)
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1755637595
|
eusuf01
| 2025-08-19T21:06:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:06:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/41937
|
crystalline7
| 2025-08-19T21:06:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:06:26Z |
[View on Civ Archive](https://civarchive.com/models/54698?modelVersionId=59074)
|
crystalline7/32891
|
crystalline7
| 2025-08-19T21:05:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:05:39Z |
[View on Civ Archive](https://civarchive.com/models/37062?modelVersionId=43090)
|
crystalline7/20731
|
crystalline7
| 2025-08-19T21:04:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:04:14Z |
[View on Civ Archive](https://civarchive.com/models/21003?modelVersionId=24998)
|
ultratopaz/39813
|
ultratopaz
| 2025-08-19T21:03:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:03:22Z |
[View on Civ Archive](https://civarchive.com/models/50821?modelVersionId=55337)
|
ultratopaz/77712
|
ultratopaz
| 2025-08-19T21:01:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:01:28Z |
[View on Civ Archive](https://civarchive.com/models/102801?modelVersionId=110019)
|
abwolf86/MyGemmaNPC
|
abwolf86
| 2025-08-19T21:00:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T20:34:45Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
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="abwolf86/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Muapi/wizard-s-paper-model-universe
|
Muapi
| 2025-08-19T20:59:26Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:58:50Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Wizard's Paper Model Universe
**Base model**: Flux.1 D
**Trained words**: A paper model
## 🧠 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:873875@978295", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/detailed-vector-illustration
|
Muapi
| 2025-08-19T20:58:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:58:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Detailed Vector Illustration
**Base model**: Flux.1 D
**Trained words**: detailed vector illustration
## 🧠 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:842234@942259", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
New-original-archita-phukan-viral-video-on/New.full.videos.archita.Phukan.Viral.Video.Official.Tutorial
|
New-original-archita-phukan-viral-video-on
| 2025-08-19T20:55:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T20:55:08Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
Muapi/ars-midjourney-style-flux
|
Muapi
| 2025-08-19T20:52:49Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:52:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Ars MidJourney Style - Flux
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:650086@727320", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
damienbenveniste/medical_assistant
|
damienbenveniste
| 2025-08-19T20:49:51Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-0.6B-Base",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-0.6B-Base",
"region:us"
] |
text-generation
| 2025-08-19T14:05:39Z |
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-0.6B-Base
- 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
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755634975
|
calegpedia
| 2025-08-19T20:49:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:49:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/UI-Venus-Navi-72B-i1-GGUF
|
mradermacher
| 2025-08-19T20:45:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:inclusionAI/UI-Venus-Navi-72B",
"base_model:quantized:inclusionAI/UI-Venus-Navi-72B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T08:41:22Z |
---
base_model: inclusionAI/UI-Venus-Navi-72B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/inclusionAI/UI-Venus-Navi-72B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UI-Venus-Navi-72B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF
**This is a vision model - mmproj files (if any) will be in the [static repository](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-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/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ1_S.gguf) | i1-IQ1_S | 22.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ1_M.gguf) | i1-IQ1_M | 23.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ2_S.gguf) | i1-IQ2_S | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ2_M.gguf) | i1-IQ2_M | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 29.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q2_K.gguf) | i1-Q2_K | 29.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 31.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.9 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ3_M.gguf) | i1-IQ3_M | 35.6 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 37.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 39.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 39.8 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q4_0.gguf) | i1-Q4_0 | 41.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 44.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q4_1.gguf) | i1-Q4_1 | 45.8 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF/resolve/main/UI-Venus-Navi-72B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 64.4 | 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 -->
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755634407
|
kojeklollipop
| 2025-08-19T20:40:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:40:00Z |
---
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).
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755635920
|
Dombili2038
| 2025-08-19T20:39:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:39:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755635808
|
roeker
| 2025-08-19T20:38:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:37:35Z |
---
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).
|
aralper18/blockassist-bc-gilded_tangled_albatross_1755635594
|
aralper18
| 2025-08-19T20:34:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded tangled albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:33:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded tangled albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/pinup-model-style-flux
|
Muapi
| 2025-08-19T20:29:49Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:29:24Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Pinup Model Style FLUX
**Base model**: Flux.1 D
**Trained words**: a pinup painting of woman, hud_p1nup_styl,_
## 🧠 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:743220@831195", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
AnjaliNV/Merged_WellBeing_LLM
|
AnjaliNV
| 2025-08-19T20:29:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T20:23:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Muapi/ultra-real-anime-flux
|
Muapi
| 2025-08-19T20:27:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:27:27Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Ultra Real Anime Flux
**Base model**: Flux.1 D
**Trained words**: ANIMEFLUX
## 🧠 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:1131779@1272367", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755632523
|
thanobidex
| 2025-08-19T20:09:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:09:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755632445
|
katanyasekolah
| 2025-08-19T20:09:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:09:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/gpt-oss-sanguine-20b-v1-GGUF
|
mradermacher
| 2025-08-19T19:59:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"peft",
"lora",
"roleplay",
"creative-writing",
"consequence-based-alignment",
"gpt-oss",
"openai-harmony",
"en",
"zh",
"dataset:NousResearch/Hermes-3-Dataset",
"dataset:Anthropic/hh-rlhf",
"dataset:teknium/OpenHermes-2.5",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:calme/legalkit",
"dataset:nvidia/Llama-3.1-Nemotron-70B-Instruct-HF",
"dataset:Yoondi/bluemoon-roleplay-chat-jsonl",
"dataset:LooksJuicy/Chinese-Roleplay-Novel",
"dataset:zhouzr/pk-roleplay",
"dataset:openerotica/long-roleplay-v0.1",
"dataset:mrcuddle/nous-character-codex",
"dataset:Arasaaf/myuri_roleplay",
"dataset:AlekseyKorshuk/gpt-roleplay-realm-chatml",
"dataset:diwank/gpt_roleplay_realm-chatml",
"dataset:Gryphe/Sonnet3.5-Charcard-Roleplay",
"dataset:hieunguyenminh/roleplay",
"dataset:zerofata/Roleplay-Anime-Characters",
"dataset:Locutusque/FalseReject-sharegpt",
"dataset:QuixiAI/open-instruct-uncensored",
"dataset:allenai/WildChat-4.8M-Full",
"dataset:nvidia/Llama-Nemotron-Post-Training-Dataset",
"dataset:WizardLMTeam/WizardLM_evol_instruct_V2_196k",
"dataset:nvidia/OpenCodeReasoning",
"dataset:MaziyarPanahi/calme-legalkit-v0.2",
"dataset:Nitral-AI/Cybersecurity-ShareGPT",
"dataset:savaniDhruv/Cybersecurity_Attack_Dataset",
"dataset:openerotica/erotica-analysis",
"dataset:demelin/moral_stories",
"base_model:paperboygold/gpt-oss-sanguine-20b-v1",
"base_model:adapter:paperboygold/gpt-oss-sanguine-20b-v1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:35:12Z |
---
base_model: paperboygold/gpt-oss-sanguine-20b-v1
datasets:
- NousResearch/Hermes-3-Dataset
- Anthropic/hh-rlhf
- teknium/OpenHermes-2.5
- microsoft/orca-math-word-problems-200k
- WizardLM/WizardLM_evol_instruct_V2_196k
- calme/legalkit
- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
- Yoondi/bluemoon-roleplay-chat-jsonl
- LooksJuicy/Chinese-Roleplay-Novel
- zhouzr/pk-roleplay
- openerotica/long-roleplay-v0.1
- mrcuddle/nous-character-codex
- Arasaaf/myuri_roleplay
- AlekseyKorshuk/gpt-roleplay-realm-chatml
- diwank/gpt_roleplay_realm-chatml
- Gryphe/Sonnet3.5-Charcard-Roleplay
- hieunguyenminh/roleplay
- zerofata/Roleplay-Anime-Characters
- Locutusque/FalseReject-sharegpt
- QuixiAI/open-instruct-uncensored
- allenai/WildChat-4.8M-Full
- nvidia/Llama-Nemotron-Post-Training-Dataset
- WizardLMTeam/WizardLM_evol_instruct_V2_196k
- nvidia/OpenCodeReasoning
- MaziyarPanahi/calme-legalkit-v0.2
- Nitral-AI/Cybersecurity-ShareGPT
- savaniDhruv/Cybersecurity_Attack_Dataset
- openerotica/erotica-analysis
- demelin/moral_stories
language:
- en
- zh
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- peft
- lora
- roleplay
- creative-writing
- consequence-based-alignment
- gpt-oss
- openai-harmony
---
## 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/paperboygold/gpt-oss-sanguine-20b-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gpt-oss-sanguine-20b-v1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-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/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q3_K_S.gguf) | Q3_K_S | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q2_K.gguf) | Q2_K | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.IQ4_XS.gguf) | IQ4_XS | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q3_K_M.gguf) | Q3_K_M | 13.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q3_K_L.gguf) | Q3_K_L | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q4_K_S.gguf) | Q4_K_S | 14.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q4_K_M.gguf) | Q4_K_M | 15.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q5_K_S.gguf) | Q5_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q5_K_M.gguf) | Q5_K_M | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-sanguine-20b-v1-GGUF/resolve/main/gpt-oss-sanguine-20b-v1.Q8_0.gguf) | Q8_0 | 22.4 | fast, best quality |
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 -->
|
mahmoudOmar03/reading_task_QA
|
mahmoudOmar03
| 2025-08-19T18:24:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T18:23:57Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mahmoudOmar03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sophie-Rain-V-iral-v-ideo-original-XX/Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial
|
Sophie-Rain-V-iral-v-ideo-original-XX
| 2025-08-19T18:23:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T18:18:11Z |
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div>
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755626307
|
lisaozill03
| 2025-08-19T18:22:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:22:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755627622
|
Dejiat
| 2025-08-19T18:21:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:20:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
UnbeT/bielik_rewardall
|
UnbeT
| 2025-08-19T18:18:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"trl",
"reward-trainer",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T18:14:29Z |
---
library_name: transformers
tags:
- trl
- reward-trainer
---
# 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]
|
MrRikyz/Impish-Irix-Kitsune-GGUF
|
MrRikyz
| 2025-08-19T18:16:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"RP",
"mistral",
"roleplay",
"nsfw",
"llama-cpp",
"base_model:MrRikyz/Impish-Irix-Kitsune",
"base_model:quantized:MrRikyz/Impish-Irix-Kitsune",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T16:17:04Z |
---
base_model: MrRikyz/Impish-Irix-Kitsune
library_name: transformers
tags:
- mergekit
- merge
- RP
- mistral
- roleplay
- nsfw
- llama-cpp
license: apache-2.0
---
## About
static quants of https://huggingface.co/MrRikyz/Impish-Irix-Kitsune
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
If you want a specific quant just ask for it in the community tab
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q2_K.gguf) | Q2_K | 4.8 | Very low quality, Not recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_S.gguf) | Q3_K_S | 5.6 | low quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-IQ3_M.gguf) | IQ3_M | 5.7 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-IQ4_XS.gguf) | IQ4_XS | 6.8 | balanced speed and quality, recomended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q4_K_S.gguf) | Q4_K_S | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q4_K_M.gguf) | Q4_K_M | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q5_K_M.gguf) | Q5_K_M | 8.8 | good quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q6_K.gguf) | Q6_K | 10.1 | very good quality |
| [GGUF](https://huggingface.co/MrRikyz/Impish-Irix-Kitsune-GGUF/resolve/main/Impish-Irix-Kitsune-Q8_0.gguf) | Q8_0 | 13.1 | best quality |
|
yk0/forge-e39
|
yk0
| 2025-08-19T18:14:12Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2025-08-19T18:11:33Z |
# forge-v1 Model
Private testing version.
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755625475
|
katanyasekolah
| 2025-08-19T18:13:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:13:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shujashark/llm-sql-t5-small-lora-adapter
|
Shujashark
| 2025-08-19T18:12:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:t5-small",
"lora",
"transformers",
"arxiv:1910.09700",
"base_model:google-t5/t5-small",
"base_model:adapter:google-t5/t5-small",
"region:us"
] | null | 2025-08-19T18:08:48Z |
---
base_model: t5-small
library_name: peft
tags:
- base_model:adapter:t5-small
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755624927
|
calegpedia
| 2025-08-19T18:02:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:02:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
g-assismoraes/Qwen3-4B-Base-aki-alpha0.08-var-adown0.05-qQ2Q3-hatebr-pos
|
g-assismoraes
| 2025-08-19T18:00:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T17:52:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
|
khawarizmiai/Khawarizmi-SPI-MLP-8B
|
khawarizmiai
| 2025-08-19T17:55:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T12:47:42Z |
---
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
license: mit
---
# Khawarizmi-SPI-MLP-8B
## Model Overview
**Khawarizmi-SPI-MLP-8B** is a hybrid language model developed by Khawarizmi AI, leveraging the innovative **Selective Parameter Interpolation on MLP Layers (SPI-MLP)** algorithm. This model ingeniously combines the robust linguistic capabilities of **Qwen3-8B** with the advanced reasoning patterns of **DeepSeek-R1**. The fusion is specifically applied to the Multi-Layer Perceptron (MLP) layers, with a composition of 60% DeepSeek and 40% Qwen, while critically preserving Qwen's original attention and normalization layers. This unique architectural approach aims to deliver a model highly proficient in complex reasoning, code generation, and multilingual tasks, with a particular emphasis on Arabic-English understanding.
## Technical Specifications
Based on the `config.json` and `generation_config.json` files, the Khawarizmi-SPI-MLP-8B model exhibits the following technical characteristics:
### Architecture and Configuration
| Parameter | Value | Description |
|---|---|---|
| `architectures` | `["Qwen3ForCausalLM"]` | Indicates the model architecture is a Causal Language Model based on Qwen3. |
| `attention_bias` | `false` | Specifies if attention bias is used. |
| `attention_dropout` | `0.0` | Dropout rate for attention layers. |
| `bos_token_id` | `151643` | Beginning-of-sequence token ID. |
| `eos_token_id` | `[151645, 151643]` | End-of-sequence token IDs. |
| `head_dim` | `128` | Dimension of each attention head. |
| `hidden_act` | `"silu"` | Activation function used in hidden layers. |
| `hidden_size` | `4096` | Dimensionality of the encoder layers and the pooler layer. |
| `initializer_range` | `0.02` | Standard deviation of the truncated normal initializer. |
| `intermediate_size` | `12288` | Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. |
| `max_position_embeddings` | `40960` | The maximum sequence length that this model might ever be used with. |
| `max_window_layers` | `36` | Maximum number of layers for windowed attention. |
| `model_type` | `"qwen3"` | The type of the model, indicating its base family. |
| `num_attention_heads` | `32` | Number of attention heads for each attention layer in the Transformer encoder. |
| `num_hidden_layers` | `36` | Number of hidden layers in the Transformer encoder. |
| `num_key_value_heads` | `8` | Number of key-value heads. |
| `rms_norm_eps` | `1e-06` | The epsilon used by the RMS normalization layers. |
| `rope_scaling` | `null` | RoPE scaling configuration. |
| `rope_theta` | `1000000` | RoPE theta value. |
| `sliding_window` | `null` | Sliding window configuration. |
| `tie_word_embeddings` | `false` | Whether to tie the word embeddings with the output layer. |
| `torch_dtype` | `"bfloat16"` | The data type used for the model parameters. |
| `transformers_version` | `"4.51.0"` | The version of the Hugging Face Transformers library used. |
| `use_cache` | `true` | Whether or not the model should return the last key/values attentions (not used by all models). |
| `use_sliding_window` | `false` | Whether to use sliding window attention. |
| `vocab_size` | `151936` | Vocabulary size of the model. |
### Generation Configuration
| Parameter | Value | Description |
|---|---|---|
| `do_sample` | `true` | Whether or not to use sampling; use greedy decoding otherwise. |
| `pad_token_id` | `151643` | Padding token ID. |
| `temperature` | `0.6` | The value used to modulate the next token probabilities. |
| `top_k` | `20` | The number of highest probability vocabulary tokens to keep for top-k-filtering. |
| `top_p` | `0.95` | If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or higher are kept for generation. |
| `transformers_version` | `"4.51.0"` | The version of the Hugging Face Transformers library used for generation. |
## Merge Strategy
The **Khawarizmi-SPI-MLP-8B** model employs a sophisticated Selective Parameter Interpolation (SPI) strategy specifically targeting the MLP layers. This method allows for a nuanced integration of two distinct models: **Qwen3-8B** and **DeepSeek-R1**. The core idea is to selectively interpolate parameters within the MLP layers, achieving a blend that harnesses the strengths of both base models while maintaining the structural integrity of Qwen's attention and normalization layers. This approach ensures that the model benefits from DeepSeek-R1's reasoning capabilities without compromising Qwen3-8B's established linguistic prowess.
For each weight tensor $W_k$:
$$
W_k^{\text{merged}} =
\begin{cases}
0.6 \cdot W_k^{\text{(DeepSeek)}} + 0.4 \cdot W_k^{\text{(Qwen)}} & \text{if "mlp" in } k \\
W_k^{\text{(Qwen)}} & \text{otherwise}
\end{cases}
$$
## How to Use
To utilize the **Khawarizmi-SPI-MLP-8B** model, follow the instructions below. Ensure you have the necessary dependencies installed.
### Installation
First, install the required Python packages using `pip`:
```bash
pip install -q transformers accelerate safetensors sentencepiece torch
```
### Model Loading and Inference
Once the dependencies are installed, you can load the model and tokenizer using the Hugging Face `transformers` library and perform text generation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "khawarizmiai/Khawarizmi-SPI-MLP-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
# Example usage (you can expand on this with more detailed examples)
# prompt = "Write a short story about a robot learning to feel."
# input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
# generated_ids = model.generate(**input_ids, max_new_tokens=100)
# print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
## Evaluation
While specific benchmark results for Khawarizmi-SPI-MLP-8B are not detailed in the provided files, the model's design, which integrates DeepSeek-R1's reasoning patterns, suggests a focus on improving performance in areas such as:
* **Reasoning**: Enhanced logical reasoning and problem-solving capabilities.
* **Code Generation**: Improved ability to generate accurate and efficient code.
* **Multilingual Tasks**: Stronger performance in understanding and generating text in multiple languages, particularly Arabic and English.
Further evaluations would be necessary to quantify the model's performance across standard benchmarks (e.g., MMLU, GSM8K, HumanEval) to provide a comprehensive understanding of its capabilities.
## Limitations
As with all large language models, Khawarizmi-SPI-MLP-8B may exhibit certain limitations inherent to current AI technology:
* **Hallucination**: The model might generate factually incorrect or nonsensical information.
* **Bias**: Potential biases present in the training data could be reflected in the model's outputs.
* **Lack of Common Sense**: The model may occasionally lack human-like common sense reasoning, leading to unexpected or illogical responses.
Users are advised to exercise caution and verify critical information generated by the model.
## License
The licensing information for Khawarizmi-SPI-MLP-8B is available in the `LICENSE` file within the repository. Users should refer to this file for detailed terms and conditions regarding the use and distribution of the model.
## Citation
If you find Khawarizmi-SPI-MLP-8B useful in your research or applications, please consider citing it. A formal citation will be provided upon publication of the research paper detailing the SPI-MLP algorithm and the model's development.
## Acknowledgements
We extend our gratitude to the open-source community and the developers of Qwen3-8B and DeepSeek-R1, whose foundational work has been instrumental in the creation of Khawarizmi-SPI-MLP-8B. Their contributions continue to drive innovation in the field of artificial intelligence.
## Contact
For inquiries, collaborations, or feedback regarding Khawarizmi-SPI-MLP-8B, please reach out to the Khawarizmi AI team through the Hugging Face platform or official channels as they become available.
## Disclaimer
Khawarizmi-SPI-MLP-8B is provided for research and experimental purposes. While efforts have been made to ensure its quality and performance, Khawarizmi AI does not guarantee its suitability for any specific application. Users are responsible for assessing the model's outputs and ensuring compliance with all applicable laws and regulations.
live
|
AppliedLucent/nemo-phase6
|
AppliedLucent
| 2025-08-19T17:49:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:AppliedLucent/nemo-phase5",
"base_model:finetune:AppliedLucent/nemo-phase5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T17:38:35Z |
---
base_model: AppliedLucent/nemo-phase5
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AppliedLucent
- **License:** apache-2.0
- **Finetuned from model :** AppliedLucent/nemo-phase5
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)
|
dgambettaphd/M_mis_run2_gen2_WXS_doc1000_synt64_lr1e-04_acm_LANG
|
dgambettaphd
| 2025-08-19T17:48:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T17:48:32Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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.
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|
forstseh/blockassist-bc-arctic_soaring_heron_1755622249
|
forstseh
| 2025-08-19T17:43:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic soaring heron",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:43:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic soaring heron
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmeisb01q0rv8rts8hagsof4l_cmeisj43g0rw9rts87hpu0q86
|
BootesVoid
| 2025-08-19T17:41:28Z | 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
| 2025-08-19T17:41:27Z |
---
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: MODELO
---
# Cmeisb01Q0Rv8Rts8Hagsof4L_Cmeisj43G0Rw9Rts87Hpu0Q86
<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 `MODELO` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MODELO",
"lora_weights": "https://huggingface.co/BootesVoid/cmeisb01q0rv8rts8hagsof4l_cmeisj43g0rw9rts87hpu0q86/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/cmeisb01q0rv8rts8hagsof4l_cmeisj43g0rw9rts87hpu0q86', weight_name='lora.safetensors')
image = pipeline('MODELO').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/cmeisb01q0rv8rts8hagsof4l_cmeisj43g0rw9rts87hpu0q86/discussions) to add images that show off what you’ve made with this LoRA.
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755625199
|
Vasya777
| 2025-08-19T17:40:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:40:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
allisterb/gemma3_270m_tools_test
|
allisterb
| 2025-08-19T17:38:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gemma-3-270m-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:13:57Z |
---
base_model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** allisterb
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it-unsloth-bnb-4bit
This gemma3_text 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)
|
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