modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 06:30:45
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-02 06:30:39
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
amethyst9/473388
|
amethyst9
| 2025-09-01T23:20:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:20:52Z |
[View on Civ Archive](https://civarchive.com/models/501457?modelVersionId=557385)
|
ultratopaz/1867667
|
ultratopaz
| 2025-09-01T23:20:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:20:26Z |
[View on Civ Archive](https://civarchive.com/models/1740880?modelVersionId=1970193)
|
ultratopaz/365148
|
ultratopaz
| 2025-09-01T23:20:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:20:10Z |
[View on Civ Archive](https://civarchive.com/models/399146?modelVersionId=445157)
|
Muapi/futuristic-display-enhancer-flux
|
Muapi
| 2025-09-01T23:19:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:19:25Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Futuristic Display Enhancer FLUX
**Base model**: Flux.1 D
**Trained words**: mad-dshbrd
## 🧠 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:826433@924212", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/517587
|
seraphimzzzz
| 2025-09-01T23:19:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:19:29Z |
[View on Civ Archive](https://civarchive.com/models/541984?modelVersionId=602602)
|
ultratopaz/328170
|
ultratopaz
| 2025-09-01T23:19:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:19:21Z |
[View on Civ Archive](https://civarchive.com/models/363096?modelVersionId=405729)
|
crystalline7/137748
|
crystalline7
| 2025-09-01T23:19:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:19:12Z |
[View on Civ Archive](https://civarchive.com/models/159930?modelVersionId=179887)
|
ultratopaz/137739
|
ultratopaz
| 2025-09-01T23:19:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:19:05Z |
[View on Civ Archive](https://civarchive.com/models/159918?modelVersionId=179872)
|
crystalline7/292637
|
crystalline7
| 2025-09-01T23:18:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:18:48Z |
[View on Civ Archive](https://civarchive.com/models/326544?modelVersionId=366009)
|
mradermacher/XLM-Prohori-v2-GGUF
|
mradermacher
| 2025-09-01T23:18:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:squadgoals404/XLM-Prohori-v2",
"base_model:quantized:squadgoals404/XLM-Prohori-v2",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-09-01T23:07:14Z |
---
base_model: squadgoals404/XLM-Prohori-v2
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/squadgoals404/XLM-Prohori-v2
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#XLM-Prohori-v2-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q5_K_S.gguf) | Q5_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q5_K_M.gguf) | Q5_K_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q6_K.gguf) | Q6_K | 0.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/XLM-Prohori-v2-GGUF/resolve/main/XLM-Prohori-v2.f16.gguf) | f16 | 0.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
casperbenya/blockassist-bc-meek_barky_macaw_1756768645
|
casperbenya
| 2025-09-01T23:18:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek barky macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:18:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek barky macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/1570531
|
seraphimzzzz
| 2025-09-01T23:18:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:18:23Z |
[View on Civ Archive](https://civarchive.com/models/1476266?modelVersionId=1669799)
|
amethyst9/1562400
|
amethyst9
| 2025-09-01T23:18:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:18:15Z |
[View on Civ Archive](https://civarchive.com/models/1469360?modelVersionId=1661927)
|
bah63843/blockassist-bc-plump_fast_antelope_1756768628
|
bah63843
| 2025-09-01T23:18:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:17:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KRLabsOrg/tinylettuce-ettin-17m-en
|
KRLabsOrg
| 2025-09-01T23:17:33Z | 9 | 1 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"token-classification",
"token classification",
"hallucination detection",
"retrieval-augmented generation",
"ettin",
"lightweight",
"en",
"dataset:ragtruth",
"dataset:KRLabsOrg/rag-bioasq-lettucedetect",
"arxiv:2507.11412",
"arxiv:2502.17125",
"base_model:jhu-clsp/ettin-encoder-17m",
"base_model:finetune:jhu-clsp/ettin-encoder-17m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-31T10:56:00Z |
---
license: mit
language:
- en
base_model:
- jhu-clsp/ettin-encoder-17m
pipeline_tag: token-classification
tags:
- token classification
- hallucination detection
- retrieval-augmented generation
- transformers
- ettin
- lightweight
datasets:
- ragtruth
- KRLabsOrg/rag-bioasq-lettucedetect
library_name: transformers
---
# TinyLettuce (Ettin-17M): Efficient Hallucination Detection
<p align="center">
<img src="https://github.com/KRLabsOrg/LettuceDetect/blob/dev/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/>
</p>
**Model Name:** tinylettuce-ettin-17m-en
**Organization:** KRLabsOrg
**Github:** https://github.com/KRLabsOrg/LettuceDetect
**Ettin encoders:** https://arxiv.org/pdf/2507.11412
## Overview
TinyLettuce is a lightweight token‑classification model that flags unsupported spans in answers given context (span aggregation performed downstream). Built on the 17M Ettin encoder, it targets real‑time CPU inference and low‑cost domain fine‑tuning with synthetic data.
This variant is trained synthetic data and on the RAGTruth dataset for hallucination detection, using the 17M Ettin encoder and a token‑classification head. Designed for CPU‑friendly inference and simple deployment.
## Model Details
- Architecture: Ettin encoder (17M) + token‑classification head
- Task: token classification (0 = supported, 1 = hallucinated)
- Input format: [CLS] context [SEP] question [SEP] answer [SEP], up to 4096 tokens
- Language: English; License: MIT
## Training Data
- RAGTruth + our synthetic data generated with LettuceDetect, span‑level labels
- ~20k training samples
## Training Procedure
- Tokenizer: AutoTokenizer; DataCollatorForTokenClassification; label pad −100
- Max length: 8k; batch size: 16; epochs: 5
- Optimizer: AdamW (lr 1e‑5, weight_decay 0.01)
- Hardware: Single A100 80GB
## Results (RAGTruth)
This model is designed primarily for fine-tuning on smaller, domain-specific samples, rather than for general use (though it still performs notably on Ragtruth).
| Model | Parameters | F1 (%) |
|-------|------------|--------|
| TinyLettuce-17M | 17M | 68.52 |
| LettuceDetect-base (ModernBERT) | 150M | 76.07 |
| LettuceDetect-large (ModernBERT) | 395M | 79.22 |
| Llama-2-13B (RAGTruth FT) | 13B | 78.70 |
## Usage
You can use the model with the **lettucedetect** library.
First install **lettucedetect**:
```bash
pip install lettucedetect
```
Then use it:
```python
from lettucedetect.models.inference import HallucinationDetector
# Load tiny but powerful model
detector = HallucinationDetector(
method="transformer",
model_path="KRLabsOrg/tinylettuce-ettin-17m-en"
)
# Detect hallucinations in medical context
spans = detector.predict(
context=[
"Ibuprofen is an NSAID that reduces inflammation and pain. The typical adult dose is 400-600mg every 6-8 hours, not exceeding 2400mg daily."
],
question="What is the maximum daily dose of ibuprofen?",
answer="The maximum daily dose of ibuprofen for adults is 3200mg.",
output_format="spans",
)
print(spans)
# Output: [{"start": 51, "end": 57, "text": "3200mg"}]
```
## Citing
If you use the model or the tool, please cite the following paper:
```bibtex
@misc{Kovacs:2025,
title={LettuceDetect: A Hallucination Detection Framework for RAG Applications},
author={Ádám Kovács and Gábor Recski},
year={2025},
eprint={2502.17125},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.17125},
}
```
|
ultratopaz/132509
|
ultratopaz
| 2025-09-01T23:17:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:17:33Z |
[View on Civ Archive](https://civarchive.com/models/155013?modelVersionId=173818)
|
Muapi/animelight-v2
|
Muapi
| 2025-09-01T23:17:18Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:17:06Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# AnimeLight-v2
**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:649847@734168", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/321442
|
seraphimzzzz
| 2025-09-01T23:17:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:17:09Z |
[View on Civ Archive](https://civarchive.com/models/356546?modelVersionId=398582)
|
ReportAId/whisper-medium-it-finetuned-without-voxpopuli
|
ReportAId
| 2025-09-01T23:17:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-01T22:37:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
amethyst9/358157
|
amethyst9
| 2025-09-01T23:17:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:17:00Z |
[View on Civ Archive](https://civarchive.com/models/392362?modelVersionId=437680)
|
Muapi/former-splendor
|
Muapi
| 2025-09-01T23:16:54Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:15:16Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Former Splendor
**Base model**: Flux.1 D
**Trained words**: FS
## 🧠 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:997539@1946460", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
amethyst9/146641
|
amethyst9
| 2025-09-01T23:16:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:44Z |
[View on Civ Archive](https://civarchive.com/models/170655?modelVersionId=191753)
|
seraphimzzzz/473378
|
seraphimzzzz
| 2025-09-01T23:16:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:36Z |
[View on Civ Archive](https://civarchive.com/models/501448?modelVersionId=557377)
|
ultratopaz/872538
|
ultratopaz
| 2025-09-01T23:16:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:28Z |
[View on Civ Archive](https://civarchive.com/models/863338?modelVersionId=965997)
|
amethyst9/350892
|
amethyst9
| 2025-09-01T23:16:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:19Z |
[View on Civ Archive](https://civarchive.com/models/385353?modelVersionId=430040)
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756768448
|
xinnn32
| 2025-09-01T23:16:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:15:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/1675883
|
seraphimzzzz
| 2025-09-01T23:16:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:11Z |
[View on Civ Archive](https://civarchive.com/models/1568652?modelVersionId=1775105)
|
crystalline7/137781
|
crystalline7
| 2025-09-01T23:16:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:16:04Z |
[View on Civ Archive](https://civarchive.com/models/159969?modelVersionId=179936)
|
seraphimzzzz/350940
|
seraphimzzzz
| 2025-09-01T23:15:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:55Z |
[View on Civ Archive](https://civarchive.com/models/385385?modelVersionId=430079)
|
ultratopaz/134419
|
ultratopaz
| 2025-09-01T23:15:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:40Z |
[View on Civ Archive](https://civarchive.com/models/156772?modelVersionId=175989)
|
seraphimzzzz/522737
|
seraphimzzzz
| 2025-09-01T23:15:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:32Z |
[View on Civ Archive](https://civarchive.com/models/546320?modelVersionId=607627)
|
amethyst9/292634
|
amethyst9
| 2025-09-01T23:15:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:07Z |
[View on Civ Archive](https://civarchive.com/models/326542?modelVersionId=366007)
|
amethyst9/146600
|
amethyst9
| 2025-09-01T23:15:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:00Z |
[View on Civ Archive](https://civarchive.com/models/170597?modelVersionId=191686)
|
ultratopaz/350946
|
ultratopaz
| 2025-09-01T23:14:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:14:36Z |
[View on Civ Archive](https://civarchive.com/models/385389?modelVersionId=430083)
|
crystalline7/152226
|
crystalline7
| 2025-09-01T23:14:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:14:28Z |
[View on Civ Archive](https://civarchive.com/models/177146?modelVersionId=198871)
|
BeitTigreAI/tigre-asr-Wav2Vec2Bert
|
BeitTigreAI
| 2025-09-01T23:14:25Z | 9 | 0 | null |
[
"safetensors",
"wav2vec2-bert",
"speech-to-text",
"tigre",
"ctc",
"beam-search",
"kenlm",
"automatic-speech-recognition",
"tig",
"license:cc-by-sa-4.0",
"region:us"
] |
automatic-speech-recognition
| 2025-08-30T00:05:47Z |
---
license: cc-by-sa-4.0
language: tig
tags:
- speech-to-text
- wav2vec2-bert
- tigre
- ctc
- beam-search
- kenlm
pipeline_tag: automatic-speech-recognition
---
|
amethyst9/476009
|
amethyst9
| 2025-09-01T23:14:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:14:12Z |
[View on Civ Archive](https://civarchive.com/models/503829?modelVersionId=560045)
|
crystalline7/358165
|
crystalline7
| 2025-09-01T23:13:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:13:55Z |
[View on Civ Archive](https://civarchive.com/models/392375?modelVersionId=437691)
|
Muapi/ethereal-alien-concept-flux-ethanar
|
Muapi
| 2025-09-01T23:12:57Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:12:49Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Ethereal Alien Concept FLUX @Ethanar
**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:822233@919453", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756768299
|
ggozzy
| 2025-09-01T23:12:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:12:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/1867724
|
ultratopaz
| 2025-09-01T23:12:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:12:44Z |
[View on Civ Archive](https://civarchive.com/models/1740938?modelVersionId=1970254)
|
ultratopaz/162272
|
ultratopaz
| 2025-09-01T23:12:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:12:29Z |
[View on Civ Archive](https://civarchive.com/models/188077?modelVersionId=211191)
|
seraphimzzzz/122707
|
seraphimzzzz
| 2025-09-01T23:12:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:12:13Z |
[View on Civ Archive](https://civarchive.com/models/146112?modelVersionId=162627)
|
seraphimzzzz/137776
|
seraphimzzzz
| 2025-09-01T23:11:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:11:57Z |
[View on Civ Archive](https://civarchive.com/models/159966?modelVersionId=179931)
|
sivakrishna123/my-jarvis-adapters
|
sivakrishna123
| 2025-09-01T23:11:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T23:11:19Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sivakrishna123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
amethyst9/1570636
|
amethyst9
| 2025-09-01T23:11:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:11:42Z |
[View on Civ Archive](https://civarchive.com/models/1476360?modelVersionId=1669907)
|
crystalline7/402045
|
crystalline7
| 2025-09-01T23:11:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:11:25Z |
[View on Civ Archive](https://civarchive.com/models/434362?modelVersionId=483828)
|
seraphimzzzz/366873
|
seraphimzzzz
| 2025-09-01T23:10:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:10:51Z |
[View on Civ Archive](https://civarchive.com/models/400753?modelVersionId=446902)
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756768186
|
omerbektass
| 2025-09-01T23:10:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:10:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/neon-paint-flux-lora
|
Muapi
| 2025-09-01T23:10:03Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:09:50Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Neon Paint Flux Lora
**Base model**: Flux.1 D
**Trained words**: nps paint luminous vector art
## 🧠 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:766400@877373", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
gortanmeat/blockassist-bc-sturdy_trotting_caribou_1756768108
|
gortanmeat
| 2025-09-01T23:09:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy trotting caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:09:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy trotting caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Derendering/InkSight-Small-p
|
Derendering
| 2025-09-01T23:08:42Z | 109 | 32 |
tf-keras
|
[
"tf-keras",
"en",
"zh",
"ja",
"vi",
"arxiv:2402.05804",
"doi:10.57967/hf/3661",
"license:apache-2.0",
"region:us"
] | null | 2024-10-30T20:06:57Z |
---
license: apache-2.0
language:
- en
- zh
- ja
- vi
---
# InkSight Small-p
From [InkSight: Offline-to-Online Handwriting Conversion by Learning to Read and Write](https://github.com/google-research/inksight)
<div style="display: flex; gap: 0.5rem; flex-wrap: wrap; margin-bottom: 1rem;">
<a href="https://research.google/blog/a-return-to-hand-written-notes-by-learning-to-read-write/">
<img src="https://img.shields.io/badge/Google_Research_Blog-333333?&logo=google&logoColor=white" alt="Google Research Blog">
</a>
<a href="https://arxiv.org/abs/2402.05804">
<img src="https://img.shields.io/badge/Read_the_Paper-4CAF50?&logo=arxiv&logoColor=white" alt="Read the Paper">
</a>
<a href="https://huggingface.co/spaces/Derendering/Model-Output-Playground">
<img src="https://img.shields.io/badge/Output_Playground-007acc?&logo=huggingface&logoColor=white" alt="Try Demo on Hugging Face">
</a>
<a href="https://charlieleee.github.io/publication/inksight/">
<img src="https://img.shields.io/badge/🔗_Project_Page-FFA500?&logo=link&logoColor=white" alt="Project Page">
</a>
<a href="https://huggingface.co/datasets/Derendering/InkSight-Derenderings">
<img src="https://img.shields.io/badge/Dataset-InkSight-40AF40?&logo=huggingface&logoColor=white" alt="Hugging Face Dataset">
</a>
<a href="https://githubtocolab.com/google-research/inksight/blob/main/colab.ipynb">
<img src="https://img.shields.io/badge/Example_Colab-F9AB00?&logo=googlecolab&logoColor=white" alt="Example colab">
</a>
</div>
<figure>
<img src="https://charlieleee.github.io/publication/inksight/inksight_animation_gif.gif" alt="InkSight word-level" style="width: 100%;">
<figcaption>The illustration on InkSight's word-level model outputs both text and digital ink through "Recognize and Derender" inference. </figcaption>
</figure>
<div style="font-size: 16px; margin-top: 20px;">
<strong style="color: red;">Notice:</strong> Please use TensorFlow and tensorflow-text between version 2.15.0 and 2.17.0. Versions later than 2.17.0 may lead to unexpected behavior. We are currently investigating these issues.
</div>
## Example Usage
```python
from huggingface_hub import from_pretrained_keras
import tensorflow_text
model = from_pretrained_keras("Derendering/InkSight-Small-p")
cf = model.signatures['serving_default']
prompt = "Derender the ink." # "Recognize and derender." or "Derender the ink: <text>"
input_text = tf.constant([prompt], dtype=tf.string)
image_encoded = tf.reshape(tf.io.encode_jpeg(np.array(image)[:, :, :3]), (1, 1))
output = cf(**{'input_text': input_text, 'image/encoded': image_encoded})
```
<span>For full usage, please refer to the notebook: </span> <a href="https://githubtocolab.com/google-research/inksight/blob/main/colab.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" style="display: inline; vertical-align: middle;"></a>
## Model and Training Summary
<table style="width:100%; border-collapse: collapse; font-family: Arial, sans-serif;">
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Model Architecture</th>
<td style="border: 1px solid #333; padding: 10px;">A multimodal sequence-to-sequence Transformer model with the mT5 encoder-decoder architecture. It takes text tokens and ViT dense image embeddings as inputs to an encoder and autoregressively predicts discrete text and ink tokens with a decoder.</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Input(s)</th>
<td style="border: 1px solid #333; padding: 10px;">A pair of image and text.</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Output(s)</th>
<td style="border: 1px solid #333; padding: 10px;">Generated digital ink and text.</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Usage</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>Application:</strong> The model is for research prototype, and the public version is released and available for the public.<br>
<strong>Known Caveats:</strong> None.
</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">System Type</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>System Description:</strong> This is a standalone model.<br>
<strong>Upstream Dependencies:</strong> None.<br>
<strong>Downstream Dependencies:</strong> None.
</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Implementation Frameworks</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>Hardware & Software:</strong> Hardware: TPU v5e.<br>
Software: T5X , JAX/Flax, Flaxformer.<br>
<strong>Compute Requirements:</strong> We train all of our models for 340k steps with batch size 512. With frozen ViT encoders, the training of Small-i takes ∼33h on 64 TPU v5e chips and the training of Large-i takes ∼105h on 64 TPU v5e chips.
</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Data Overview</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>Training Datasets:</strong> The ViT encoder of Small-p is pretrained on ImageNet-21k, mT5 encoder and decoder are initialized from scratch. The entire model is trained on the mixture of publicly available datasets described in next section.
</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Evaluation Results</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>Evaluation Methods:</strong> Human evaluation (reported in Section 4.5.1 of the paper) and automated evaluations (reported in Section 4.5.2 of the paper).
</td>
</tr>
<tr>
<th style="width: 30%; border: 1px solid #333; padding: 10px;">Model Usage & Limitations</th>
<td style="border: 1px solid #333; padding: 10px;">
<strong>Sensitive Use:</strong> The model is capable of converting images to digital inks. This model should not be used for any of the privacy-intruding use cases, e.g., forging handwritings.<br>
<strong>Known Limitations:</strong> Reported in Appendix I of the paper.<br>
<strong>Ethical Considerations & Potential Societal Consequences:</strong> Reported in Sections 6.1 and 6.2 of the paper.
</td>
</tr>
</table>
## Citation
If you find our work useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{
mitrevski2025inksight,
title={InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write},
author={Blagoj Mitrevski and Arina Rak and Julian Schnitzler and Chengkun Li and Andrii Maksai and Jesse Berent and Claudiu Cristian Musat},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=pSyUfV5BqA},
note={}
}
```
|
moyixiao/Qwen3-0.6B-GRPO-f16-150
|
moyixiao
| 2025-09-01T23:05:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T23:04:54Z |
---
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]
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756767832
|
omerbektass
| 2025-09-01T23:04:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:04:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/general-grievous-star-wars-1.5-sdxl-flux
|
Muapi
| 2025-09-01T23:03:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:02:27Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# General Grievous - Star Wars (1.5 / SDXL / FLUX)
**Base model**: Flux.1 D
**Trained words**: GeneralGrievous
## 🧠 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:201697@736567", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756767685
|
bah63843
| 2025-09-01T23:02:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:02:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
moscowx21/blockassist-bc-extinct_bipedal_clam_1756767707
|
moscowx21
| 2025-09-01T23:02:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"extinct bipedal clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:02:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- extinct bipedal clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1756767632
|
vendi11
| 2025-09-01T23:01:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:01:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756767537
|
ggozzy
| 2025-09-01T23:00:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:00:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gortanmeat/blockassist-bc-sturdy_trotting_caribou_1756767542
|
gortanmeat
| 2025-09-01T22:59:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy trotting caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:59:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy trotting caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-shaggy_melodic_cobra_1756767498
|
AnerYubo
| 2025-09-01T22:58:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy melodic cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:58:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy melodic cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756767474
|
omerbektass
| 2025-09-01T22:58:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:58:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF
|
mradermacher
| 2025-09-01T22:57:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"lfm2",
"trl",
"en",
"ml",
"base_model:Praha-Labs/LFM-MALAYALAM-TTS-v0.1",
"base_model:quantized:Praha-Labs/LFM-MALAYALAM-TTS-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T22:53:14Z |
---
base_model: Praha-Labs/LFM-MALAYALAM-TTS-v0.1
language:
- en
- ml
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2
- trl
---
## 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/Praha-Labs/LFM-MALAYALAM-TTS-v0.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#LFM-MALAYALAM-TTS-v0.1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LFM-MALAYALAM-TTS-v0.1-GGUF/resolve/main/LFM-MALAYALAM-TTS-v0.1.f16.gguf) | f16 | 0.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/FACT-1-GGUF
|
mradermacher
| 2025-09-01T22:57:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:joel-crasto/FACT-1",
"base_model:quantized:joel-crasto/FACT-1",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T22:55:18Z |
---
base_model: joel-crasto/FACT-1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## 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/joel-crasto/FACT-1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#FACT-1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/FACT-1-GGUF/resolve/main/FACT-1.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q5_K_S.gguf) | Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q5_K_M.gguf) | Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/FACT-1-GGUF/resolve/main/FACT-1.f16.gguf) | f16 | 0.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756767284
|
ggozzy
| 2025-09-01T22:56:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:55:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ypszn/blockassist-bc-yapping_pawing_worm_1756767178
|
ypszn
| 2025-09-01T22:53:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:53:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756767157
|
bah63843
| 2025-09-01T22:53:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:53:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756767133
|
omerbektass
| 2025-09-01T22:52:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:52:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1756767094
|
vendi11
| 2025-09-01T22:52:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gortanmeat/blockassist-bc-sturdy_trotting_caribou_1756767045
|
gortanmeat
| 2025-09-01T22:51:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy trotting caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:51:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy trotting caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756767034
|
klmdr22
| 2025-09-01T22:51:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:51:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JaspervanLeuven/smol_T1_E50_pick_and_place_servo_29_08_2025
|
JaspervanLeuven
| 2025-09-01T22:48:12Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:JaspervanLeuven/T1_E50_pick_and_place_servo_29_08_2025",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T22:48:00Z |
---
base_model: lerobot/smolvla_base
datasets: JaspervanLeuven/T1_E50_pick_and_place_servo_29_08_2025
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
CHRISPI09/blockassist-bc-galloping_thick_tuna_1756766869
|
CHRISPI09
| 2025-09-01T22:48:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping thick tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:48:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping thick tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-insectivorous_bold_lion_1756766867
|
akirafudo
| 2025-09-01T22:48:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:48:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
javdrher/decide-decision-classifier
|
javdrher
| 2025-09-01T22:47:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-01T21:19:44Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: decide-decision-classifier
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. -->
# decide-decision-classifier
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5161
- Accuracy: 0.8295
- Precision: 0.7990
- Recall: 0.8295
- F1: 0.8137
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 37 | 0.5644 | 0.7752 | 0.7577 | 0.7752 | 0.7628 |
| No log | 2.0 | 74 | 0.5161 | 0.8295 | 0.7990 | 0.8295 | 0.8137 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756766774
|
ggozzy
| 2025-09-01T22:47:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:47:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756765305
|
koloni
| 2025-09-01T22:47:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:47:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
csikasote/mms-1b-all-swagen-female-15hrs-52
|
csikasote
| 2025-09-01T22:47:03Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"swagen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-01T22:16:55Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- swagen
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-swagen-female-15hrs-52
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. -->
# mms-1b-all-swagen-female-15hrs-52
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the SWAGEN - SWA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2575
- Wer: 0.2260
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 52
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 200
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 7.1807 | 0.1572 | 200 | 1.8716 | 0.9711 |
| 1.6673 | 0.3145 | 400 | 0.2982 | 0.2073 |
| 1.286 | 0.4717 | 600 | 0.2890 | 0.2100 |
| 1.3054 | 0.6289 | 800 | 0.2896 | 0.2131 |
| 1.2144 | 0.7862 | 1000 | 0.2886 | 0.2177 |
| 1.1739 | 0.9434 | 1200 | 0.2815 | 0.2181 |
| 1.1605 | 1.1006 | 1400 | 0.2796 | 0.2176 |
| 1.0902 | 1.2579 | 1600 | 0.2798 | 0.2214 |
| 1.1329 | 1.4151 | 1800 | 0.2760 | 0.2266 |
| 1.0894 | 1.5723 | 2000 | 0.2626 | 0.2299 |
| 1.0737 | 1.7296 | 2200 | 0.2607 | 0.2341 |
| 1.0698 | 1.8868 | 2400 | 0.2576 | 0.2260 |
| 1.0905 | 2.0440 | 2600 | 0.2542 | 0.2295 |
| 1.0489 | 2.2013 | 2800 | 0.2555 | 0.2307 |
| 1.0234 | 2.3585 | 3000 | 0.2594 | 0.2365 |
| 1.0368 | 2.5157 | 3200 | 0.2551 | 0.2357 |
| 1.0381 | 2.6730 | 3400 | 0.2543 | 0.2316 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756765198
|
helmutsukocok
| 2025-09-01T22:44:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:44:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
emaanbilal/mistral_7b_legal_fft
|
emaanbilal
| 2025-09-01T22:42:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T15:39:13Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: transformers
model_name: mistral_7b_legal_fft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for mistral_7b_legal_fft
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
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="emaanbilal/mistral_7b_legal_fft", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/emaanbilal-aag/full-finetuning-medical/runs/07voavl9)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.54.1
- Pytorch: 2.7.1+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gortanmeat/blockassist-bc-sturdy_trotting_caribou_1756766506
|
gortanmeat
| 2025-09-01T22:42:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy trotting caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:42:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy trotting caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CHRISPI09/blockassist-bc-galloping_thick_tuna_1756766524
|
CHRISPI09
| 2025-09-01T22:42:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping thick tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:42:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping thick tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756766436
|
klmdr22
| 2025-09-01T22:41:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:41:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sergSt/flux-lora
|
sergSt
| 2025-09-01T22:41:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion",
"lora",
"fluxart",
"image-generation",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-09-01T18:02:29Z |
---
tags:
- stable-diffusion
- lora
- fluxart
- image-generation
license: creativeml-openrail-m
base_model: FluxArt/stable-flux-v1.5
library_name: diffusers
pipeline_tag: text-to-image
---
# Flux LoRA v0.004
LoRA-модификация для модели [FluxArt/stable-flux-v1.5](https://huggingface.co/FluxArt/stable-flux-v1.5), предназначенная для генерации кинематографичных портретов в стиле киберпанк, неон-нуар и sci-fi.
## 🧠 Использование
```python
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("FluxArt/stable-flux-v1.5").to("cuda")
pipe.load_lora_weights("sergSt/flux-lora", weight_name="flux-lora-000004.safetensors")
pipe.fuse_lora()
image = pipe("cinematic portrait of a cyberpunk samurai").images[0]
image.save("output.png")
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756763927
|
acidjp
| 2025-09-01T22:39:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:39:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CHRISPI09/blockassist-bc-galloping_thick_tuna_1756766321
|
CHRISPI09
| 2025-09-01T22:39:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping thick tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:38:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping thick tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756766266
|
ggozzy
| 2025-09-01T22:39:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:38:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thyYu2024/dnus_22
|
thyYu2024
| 2025-09-01T22:38:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T21:43:56Z |
---
base_model: Qwen/Qwen2-VL-2B-Instruct
library_name: transformers
model_name: dnus_22
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for dnus_22
This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
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="thyYu2024/dnus_22", 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: 0.20.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu118
- 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}}
}
```
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756766160
|
liukevin666
| 2025-09-01T22:37:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:37:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CHRISPI09/blockassist-bc-galloping_thick_tuna_1756766138
|
CHRISPI09
| 2025-09-01T22:36:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping thick tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:35:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping thick tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-insectivorous_bold_lion_1756766135
|
akirafudo
| 2025-09-01T22:35:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:35:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boopmoor/blockassist-bc-winged_bipedal_quail_1756766093
|
boopmoor
| 2025-09-01T22:35:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged bipedal quail",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:34:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged bipedal quail
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756766027
|
omerbektass
| 2025-09-01T22:34:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:34:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756765900
|
xinnn32
| 2025-09-01T22:33:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:33:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
csikasote/mms-1b-all-swagen-female-15hrs-62
|
csikasote
| 2025-09-01T22:32:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"swagen",
"mms",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"base_model:finetune:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-01T22:09:15Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- automatic-speech-recognition
- swagen
- mms
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-1b-all-swagen-female-15hrs-62
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. -->
# mms-1b-all-swagen-female-15hrs-62
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the SWAGEN - SWA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2764
- Wer: 0.2177
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 62
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 200
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 7.8829 | 0.1572 | 200 | 3.9628 | 0.9998 |
| 3.5591 | 0.3145 | 400 | 3.2718 | 1.0002 |
| 3.1338 | 0.4717 | 600 | 2.9476 | 0.9992 |
| 2.2249 | 0.6289 | 800 | 0.3619 | 0.2176 |
| 1.4148 | 0.7862 | 1000 | 0.2835 | 0.2199 |
| 1.2068 | 0.9434 | 1200 | 0.2764 | 0.2181 |
| 1.1452 | 1.1006 | 1400 | 0.2731 | 0.2197 |
| 1.1111 | 1.2579 | 1600 | 0.2741 | 0.2216 |
| 1.1289 | 1.4151 | 1800 | 0.2698 | 0.2285 |
| 1.1383 | 1.5723 | 2000 | 0.2735 | 0.2334 |
| 1.0541 | 1.7296 | 2200 | 0.2727 | 0.2299 |
| 1.0778 | 1.8868 | 2400 | 0.2713 | 0.2309 |
| 1.0617 | 2.0440 | 2600 | 0.2720 | 0.2359 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
klmdr22/blockassist-bc-wild_loud_newt_1756765899
|
klmdr22
| 2025-09-01T22:32:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:32:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boopmoor/blockassist-bc-reclusive_deadly_scorpion_1756765819
|
boopmoor
| 2025-09-01T22:30:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive deadly scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:30:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive deadly scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-insectivorous_bold_lion_1756765783
|
akirafudo
| 2025-09-01T22:30:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:30:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ypszn/blockassist-bc-yapping_pawing_worm_1756765740
|
ypszn
| 2025-09-01T22:29:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:29:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/flux-concept-vehicle-2d-rendering-lora-flux-lora
|
Muapi
| 2025-09-01T22:29:01Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T22:28:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux concept vehicle 2d rendering lora FLUX概念载具渲染lora
**Base model**: Flux.1 D
**Trained words**: oue style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:651930@729346", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
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