modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 06:31:37
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-12 06:31:07
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
crystalline7/55160
|
crystalline7
| 2025-08-19T22:06:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:06:47Z |
[View on Civ Archive](https://civarchive.com/models/75657?modelVersionId=80415)
|
seraphimzzzz/481012
|
seraphimzzzz
| 2025-08-19T22:06:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:06:35Z |
[View on Civ Archive](https://civarchive.com/models/498376?modelVersionId=554000)
|
crystalline7/55386
|
crystalline7
| 2025-08-19T22:06:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:06:16Z |
[View on Civ Archive](https://civarchive.com/models/75729?modelVersionId=80767)
|
roeker/blockassist-bc-quick_wiry_owl_1755641094
|
roeker
| 2025-08-19T22:06:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:05:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/79791
|
crystalline7
| 2025-08-19T22:05:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:55Z |
[View on Civ Archive](https://civarchive.com/models/18663?modelVersionId=112521)
|
crystalline7/38474
|
crystalline7
| 2025-08-19T22:05:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:48Z |
[View on Civ Archive](https://civarchive.com/models/18663?modelVersionId=52891)
|
crystalline7/59112
|
crystalline7
| 2025-08-19T22:05:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:29Z |
[View on Civ Archive](https://civarchive.com/models/81499?modelVersionId=86483)
|
ultratopaz/55306
|
ultratopaz
| 2025-08-19T22:05:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:05:13Z |
[View on Civ Archive](https://civarchive.com/models/75923?modelVersionId=80659)
|
chansung/Gemma2-2B-CCRL-CUR-COMPLEX-ONLY-1E
|
chansung
| 2025-08-19T22:04:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:chansung/verifiable-coding-problems-python-v2",
"arxiv:2402.03300",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T02:44:25Z |
---
base_model: google/gemma-2-2b-it
datasets: chansung/verifiable-coding-problems-python-v2
library_name: transformers
model_name: Gemma2-2B-CCRL-CUR-COMPLEX-ONLY-1E
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Gemma2-2B-CCRL-CUR-COMPLEX-ONLY-1E
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chansung/Gemma2-2B-CCRL-CUR-COMPLEX-ONLY-1E", 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/chansung18/huggingface/runs/q0xteiho)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
seraphimzzzz/8148
|
seraphimzzzz
| 2025-08-19T22:04:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:04:23Z |
[View on Civ Archive](https://civarchive.com/models/7009?modelVersionId=8237)
|
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
|
crystalline7/845376
|
crystalline7
| 2025-08-19T22:04:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:04:16Z |
[View on Civ Archive](https://civarchive.com/models/558117?modelVersionId=938039)
|
crystalline7/61201
|
crystalline7
| 2025-08-19T22:04:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:04:05Z |
[View on Civ Archive](https://civarchive.com/models/83857?modelVersionId=89127)
|
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)
|
Muapi/art-nouveau-flux-lora
|
Muapi
| 2025-08-19T22:03:53Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:03:40Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Art Nouveau - Flux Lora
**Base model**: Flux.1 D
**Trained words**: art nouveau illustration, vintage ( no need specific key word to work )
## 🧠 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:638308@714072", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/87766
|
seraphimzzzz
| 2025-08-19T22:03:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:49Z |
[View on Civ Archive](https://civarchive.com/models/109244?modelVersionId=122008)
|
ultratopaz/81276
|
ultratopaz
| 2025-08-19T22:03:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:42Z |
[View on Civ Archive](https://civarchive.com/models/106428?modelVersionId=114295)
|
xfu20/BEMGPT_tp4
|
xfu20
| 2025-08-19T22:03:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T20:09:05Z |
---
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/zavy-s-aerial-view-flux
|
Muapi
| 2025-08-19T22:03:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:03:00Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Zavy's Aerial View - Flux
**Base model**: Flux.1 D
**Trained words**: zavy-rlvw
## 🧠 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:738003@825335", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755639348
|
ihsanridzi
| 2025-08-19T22:02:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:02:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/70184
|
crystalline7
| 2025-08-19T22:02:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:02:37Z |
[View on Civ Archive](https://civarchive.com/models/94194?modelVersionId=100485)
|
mradermacher/QiMing-Holos-Plus-4B-GGUF
|
mradermacher
| 2025-08-19T22:02:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen",
"qwen3",
"unsloth",
"qiming",
"qiming-holos",
"bagua",
"decision-making",
"strategic-analysis",
"cognitive-architecture",
"chat",
"lora",
"philosophy-driven-ai",
"zh",
"en",
"base_model:aifeifei798/QiMing-Holos-Plus-4B",
"base_model:adapter:aifeifei798/QiMing-Holos-Plus-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T20:13:11Z |
---
base_model: aifeifei798/QiMing-Holos-Plus-4B
language:
- zh
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- qwen
- qwen3
- unsloth
- qiming
- qiming-holos
- bagua
- decision-making
- strategic-analysis
- cognitive-architecture
- chat
- lora
- philosophy-driven-ai
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/aifeifei798/QiMing-Holos-Plus-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#QiMing-Holos-Plus-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/QiMing-Holos-Plus-4B-GGUF/resolve/main/QiMing-Holos-Plus-4B.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
crystalline7/17902
|
crystalline7
| 2025-08-19T22:02:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:02:12Z |
[View on Civ Archive](https://civarchive.com/models/18151?modelVersionId=21479)
|
mradermacher/Genuine-7B-Instruct-i1-GGUF
|
mradermacher
| 2025-08-19T22:02:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"lora",
"sft",
"trl",
"unsloth",
"fine-tuned",
"en",
"dataset:theprint/Gentle-Pushback-8.5k-alpaca",
"base_model:theprint/Genuine-7B-Instruct",
"base_model:adapter:theprint/Genuine-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T20:42:47Z |
---
base_model: theprint/Genuine-7B-Instruct
datasets:
- theprint/Gentle-Pushback-8.5k-alpaca
language: en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- lora
- sft
- transformers
- trl
- unsloth
- fine-tuned
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/theprint/Genuine-7B-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Genuine-7B-Instruct-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Genuine-7B-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Genuine-7B-Instruct-i1-GGUF/resolve/main/Genuine-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Muapi/ob-miniature-real-photography-v3
|
Muapi
| 2025-08-19T22:02:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:01:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# OB Miniature Real Photography-V3
**Base model**: Flux.1 D
**Trained words**: OBweisuo
## 🧠 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:528743@835743", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
KoichiYasuoka/modernbert-base-ukrainian
|
KoichiYasuoka
| 2025-08-19T22:02:09Z | 0 | 0 | null |
[
"pytorch",
"modernbert",
"ukrainian",
"masked-lm",
"fill-mask",
"uk",
"dataset:Goader/kobza",
"license:apache-2.0",
"region:us"
] |
fill-mask
| 2025-08-19T22:00:55Z |
---
language:
- "uk"
tags:
- "ukrainian"
- "masked-lm"
datasets:
- "Goader/kobza"
license: "apache-2.0"
pipeline_tag: "fill-mask"
mask_token: "<mask>"
---
# modernbert-base-ukrainian
## Model Description
This is a ModernBERT model pre-trained on Ukrainian texts. NVIDIA A100-SXM4-40GB×8 took 222 hours 58 minutes for training. You can fine-tune `modernbert-base-ukrainian` for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/modernbert-base-ukrainian")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/modernbert-base-ukrainian")
```
|
Muapi/cyberpunk-style-enhancer-flux
|
Muapi
| 2025-08-19T22:01:46Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T22:01:29Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 🌀 Cyberpunk Style Enhancer [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:890818@996849", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/56525
|
ultratopaz
| 2025-08-19T22:01:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:01:37Z |
[View on Civ Archive](https://civarchive.com/models/44324?modelVersionId=82580)
|
ultratopaz/36398
|
ultratopaz
| 2025-08-19T22:01:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:01:30Z |
[View on Civ Archive](https://civarchive.com/models/44324?modelVersionId=48961)
|
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)
|
ultratopaz/26699
|
ultratopaz
| 2025-08-19T22:01:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:01:00Z |
[View on Civ Archive](https://civarchive.com/models/27081?modelVersionId=32408)
|
seraphimzzzz/54659
|
seraphimzzzz
| 2025-08-19T22:00:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:40Z |
[View on Civ Archive](https://civarchive.com/models/73936?modelVersionId=79631)
|
Muapi/xenomorph-xl-sd1.5-f1d
|
Muapi
| 2025-08-19T22:00:44Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:58:51Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Xenomorph XL + SD1.5 + F1D
**Base model**: Flux.1 D
**Trained words**: Xenomorph 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:388478@1105778", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
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
|
AnonymousCS/xlmr_immigration_combo4_4
|
AnonymousCS
| 2025-08-19T22:00:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T21:56:58Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo4_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo4_4
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1633
- Accuracy: 0.9409
- 1-f1: 0.9091
- 1-recall: 0.8880
- 1-precision: 0.9312
- Balanced Acc: 0.9276
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1976 | 1.0 | 25 | 0.1552 | 0.9409 | 0.9129 | 0.9305 | 0.8959 | 0.9383 |
| 0.2233 | 2.0 | 50 | 0.1788 | 0.9306 | 0.8989 | 0.9266 | 0.8727 | 0.9296 |
| 0.0894 | 3.0 | 75 | 0.1633 | 0.9409 | 0.9091 | 0.8880 | 0.9312 | 0.9276 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
seraphimzzzz/11524
|
seraphimzzzz
| 2025-08-19T22:00:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:01Z |
[View on Civ Archive](https://civarchive.com/models/10760?modelVersionId=12772)
|
ultratopaz/71792
|
ultratopaz
| 2025-08-19T21:59:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:54Z |
[View on Civ Archive](https://civarchive.com/models/95919?modelVersionId=102431)
|
roeker/blockassist-bc-quick_wiry_owl_1755640687
|
roeker
| 2025-08-19T21:59:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:58:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/54677
|
seraphimzzzz
| 2025-08-19T21:59:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:20Z |
[View on Civ Archive](https://civarchive.com/models/36902?modelVersionId=42935)
|
seraphimzzzz/45091
|
seraphimzzzz
| 2025-08-19T21:59:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:13Z |
[View on Civ Archive](https://civarchive.com/models/59703?modelVersionId=64152)
|
ultratopaz/72344
|
ultratopaz
| 2025-08-19T21:58:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:58:55Z |
[View on Civ Archive](https://civarchive.com/models/48727?modelVersionId=103126)
|
crystalline7/80244
|
crystalline7
| 2025-08-19T21:58:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:58:49Z |
[View on Civ Archive](https://civarchive.com/models/105393?modelVersionId=113058)
|
lautan/blockassist-bc-gentle_patterned_goat_1755639114
|
lautan
| 2025-08-19T21:58:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:58:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/39163
|
ultratopaz
| 2025-08-19T21:58:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:58:36Z |
[View on Civ Archive](https://civarchive.com/models/49522?modelVersionId=54098)
|
faizack/lora-imdb-binary
|
faizack
| 2025-08-19T21:58:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T21:58:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755639097
|
hakimjustbao
| 2025-08-19T21:58:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:58:31Z |
---
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).
|
Muapi/flux-steampunk-magic
|
Muapi
| 2025-08-19T21:58:18Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:58:07Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FLUX Steampunk Magic
**Base model**: Flux.1 D
**Trained words**: bo-steampunk, steampunk 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:734196@821032", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/araminta-s-glamourphotography-sdxl-flux
|
Muapi
| 2025-08-19T21:58:02Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:57:45Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# araminta-s-glamourphotography (SDXL+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:582369@772166", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/75214
|
ultratopaz
| 2025-08-19T21:57:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:57:38Z |
[View on Civ Archive](https://civarchive.com/models/99809?modelVersionId=106824)
|
seraphimzzzz/46722
|
seraphimzzzz
| 2025-08-19T21:57:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:57:30Z |
[View on Civ Archive](https://civarchive.com/models/62174?modelVersionId=66712)
|
Muapi/flux.1-d-realistic-genshin-impact-cosplay-official-doujin-costume-collection-cosplay
|
Muapi
| 2025-08-19T21:57:24Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:57:08Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux.1 D][Realistic] <Genshin Impact>Cosplay(official/doujin) costume collection|原神cosplay(官设/同人)服装集合
**Base model**: Flux.1 D
**Trained words**: A realistic photo of a tall and slender beautiful young woman in cyb-skirk cosplay costume. She is also wearing tight elbow gloves and tight thighhighs and cosplay high heel boots. She has long white hair with hair ornament. Her one hand is holding a sword.
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:863510@2053640", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/63682
|
seraphimzzzz
| 2025-08-19T21:57:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:57:06Z |
[View on Civ Archive](https://civarchive.com/models/72365?modelVersionId=92350)
|
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/the-ai-colab
|
Muapi
| 2025-08-19T21:56:41Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:56:29Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# The AI Colab
**Base model**: Flux.1 D
**Trained words**: By theaicolab
## 🧠 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:1285923@1261262", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/15453
|
seraphimzzzz
| 2025-08-19T21:56:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:56:00Z |
[View on Civ Archive](https://civarchive.com/models/15653?modelVersionId=18465)
|
ultratopaz/72224
|
ultratopaz
| 2025-08-19T21:55:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:55:52Z |
[View on Civ Archive](https://civarchive.com/models/96401?modelVersionId=102969)
|
Muapi/john-everett-millais-style
|
Muapi
| 2025-08-19T21:55:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:55:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# John Everett Millais Style
**Base model**: Flux.1 D
**Trained words**: John Everett Millais 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:101247@1577804", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crystalline7/82522
|
crystalline7
| 2025-08-19T21:55:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:55:17Z |
[View on Civ Archive](https://civarchive.com/models/107606?modelVersionId=115748)
|
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())
```
|
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)
|
crystalline7/91801
|
crystalline7
| 2025-08-19T21:54:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:37Z |
[View on Civ Archive](https://civarchive.com/models/117216?modelVersionId=126979)
|
ultratopaz/92042
|
ultratopaz
| 2025-08-19T21:54:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:30Z |
[View on Civ Archive](https://civarchive.com/models/117436?modelVersionId=127276)
|
crystalline7/77876
|
crystalline7
| 2025-08-19T21:54:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:13Z |
[View on Civ Archive](https://civarchive.com/models/37392?modelVersionId=110230)
|
seraphimzzzz/33020
|
seraphimzzzz
| 2025-08-19T21:54:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:54:02Z |
[View on Civ Archive](https://civarchive.com/models/37392?modelVersionId=43399)
|
seraphimzzzz/77913
|
seraphimzzzz
| 2025-08-19T21:53:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:53:54Z |
[View on Civ Archive](https://civarchive.com/models/38389?modelVersionId=110283)
|
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).
|
ultratopaz/18844
|
ultratopaz
| 2025-08-19T21:53:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:53:25Z |
[View on Civ Archive](https://civarchive.com/models/19092?modelVersionId=22655)
|
koloni/blockassist-bc-deadly_graceful_stingray_1755638796
|
koloni
| 2025-08-19T21:53:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:53:20Z |
---
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).
|
Tavernari/git-commit-message-splitter-Qwen3-8B
|
Tavernari
| 2025-08-19T21:53:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T20:43:42Z |
---
base_model: unsloth/qwen3-8b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Tavernari
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b
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)
|
seraphimzzzz/40018
|
seraphimzzzz
| 2025-08-19T21:53:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:53:04Z |
[View on Civ Archive](https://civarchive.com/models/51233?modelVersionId=55724)
|
ultratopaz/75553
|
ultratopaz
| 2025-08-19T21:52:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:52:53Z |
[View on Civ Archive](https://civarchive.com/models/100222?modelVersionId=107269)
|
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).
|
ultratopaz/79678
|
ultratopaz
| 2025-08-19T21:52:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:52:13Z |
[View on Civ Archive](https://civarchive.com/models/104692?modelVersionId=112393)
|
crystalline7/77331
|
crystalline7
| 2025-08-19T21:51:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:51:42Z |
[View on Civ Archive](https://civarchive.com/models/102367?modelVersionId=109530)
|
ultratopaz/63901
|
ultratopaz
| 2025-08-19T21:51:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:51:34Z |
[View on Civ Archive](https://civarchive.com/models/87060?modelVersionId=92625)
|
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).
|
ultratopaz/755266
|
ultratopaz
| 2025-08-19T21:51:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:51:13Z |
[View on Civ Archive](https://civarchive.com/models/749996?modelVersionId=838704)
|
ver-videos-intimo-de-abigail-lalama-viral/link.ver.filtrado.video.de.abigail.lalama.y.snayder.influencer.se.hace.viral.en.redes.sociales
|
ver-videos-intimo-de-abigail-lalama-viral
| 2025-08-19T21:50:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:50:45Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?Abigail
"><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/watercolor-hand-drawn-architecture
|
Muapi
| 2025-08-19T21:50:18Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:50:04Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Watercolor hand drawn architecture
**Base model**: Flux.1 D
**Trained words**: Watercolor, hand drawn
## 🧠 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:713397@797871", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
AnonymousCS/xlmr_immigration_combo4_2
|
AnonymousCS
| 2025-08-19T21:50:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T21:37:55Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo4_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmr_immigration_combo4_2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3071
- Accuracy: 0.8946
- 1-f1: 0.8373
- 1-recall: 0.8147
- 1-precision: 0.8612
- Balanced Acc: 0.8746
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6244 | 1.0 | 25 | 0.6288 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2443 | 2.0 | 50 | 0.3320 | 0.8933 | 0.8230 | 0.7452 | 0.9190 | 0.8562 |
| 0.1623 | 3.0 | 75 | 0.2972 | 0.8997 | 0.8458 | 0.8263 | 0.8664 | 0.8813 |
| 0.1675 | 4.0 | 100 | 0.2989 | 0.8972 | 0.8431 | 0.8301 | 0.8566 | 0.8804 |
| 0.1771 | 5.0 | 125 | 0.3071 | 0.8946 | 0.8373 | 0.8147 | 0.8612 | 0.8746 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
coastalcph/Qwen2.5-7B-5t_diff_sycophant
|
coastalcph
| 2025-08-19T21:50:10Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-19T21:47:54Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-7B-Instruct")
t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-non-sycophancy")
t_combined = 1.0 * t_1 + 5.0 * t_2 - 5.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 1: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-non-sycophancy
Technical Details
- Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model1": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model2": "coastalcph/Qwen2.5-7B-personality-non-sycophancy",
"finetuned_model3": "coastalcph/Qwen2.5-7B-personality-sycophancy",
"output_model_name": "coastalcph/Qwen2.5-7B-5t_diff_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"scale_t1": 1.0,
"scale_t2": 5.0,
"scale_t3": 5.0
}
|
ultratopaz/458513
|
ultratopaz
| 2025-08-19T21:50:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:50:02Z |
[View on Civ Archive](https://civarchive.com/models/236627?modelVersionId=542199)
|
crystalline7/635547
|
crystalline7
| 2025-08-19T21:49:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:49:33Z |
[View on Civ Archive](https://civarchive.com/models/644492?modelVersionId=720947)
|
finalform/temp
|
finalform
| 2025-08-19T21:49:33Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] |
text-generation
| 2025-08-19T21:48:26Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
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)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755640133
|
lilTAT
| 2025-08-19T21:49:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:49:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/74914
|
seraphimzzzz
| 2025-08-19T21:48:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:49Z |
[View on Civ Archive](https://civarchive.com/models/99427?modelVersionId=106398)
|
seraphimzzzz/54317
|
seraphimzzzz
| 2025-08-19T21:48:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:42Z |
[View on Civ Archive](https://civarchive.com/models/74360?modelVersionId=79074)
|
seraphimzzzz/74583
|
seraphimzzzz
| 2025-08-19T21:48:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:34Z |
[View on Civ Archive](https://civarchive.com/models/99090?modelVersionId=106011)
|
ultratopaz/96557
|
ultratopaz
| 2025-08-19T21:48:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:09Z |
[View on Civ Archive](https://civarchive.com/models/121962?modelVersionId=132763)
|
Muapi/1990-s-style-xl-f1d
|
Muapi
| 2025-08-19T21:48:07Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:46:43Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 1990's style XL + F1D
**Base model**: Flux.1 D
**Trained words**: 1990 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:376915@894112", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Coercer/BatchTagger
|
Coercer
| 2025-08-19T21:48:04Z | 1 | 0 | null |
[
"region:us"
] | null | 2025-02-10T16:01:55Z |
If you got here, you might be searching for this:
Colab Implementation, where this specific repo is used.
https://colab.research.google.com/drive/1DKT5rFBTHhkyibVMK4SCYTJWHl2kaV3p?usp=sharing
Original implementation:
https://huggingface.co/RedRocket/JointTaggerProject
All credit goes to them.
|
ultratopaz/53664
|
ultratopaz
| 2025-08-19T21:47:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:47:56Z |
[View on Civ Archive](https://civarchive.com/models/73244?modelVersionId=77959)
|
ultratopaz/70921
|
ultratopaz
| 2025-08-19T21:47:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:47:50Z |
[View on Civ Archive](https://civarchive.com/models/95052?modelVersionId=101410)
|
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)
|
seraphimzzzz/65369
|
seraphimzzzz
| 2025-08-19T21:47:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:47:36Z |
[View on Civ Archive](https://civarchive.com/models/88761?modelVersionId=94447)
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755638482
|
thanobidex
| 2025-08-19T21:47:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:47:34Z |
---
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).
|
ultratopaz/67078
|
ultratopaz
| 2025-08-19T21:47:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:47:28Z |
[View on Civ Archive](https://civarchive.com/models/90656?modelVersionId=96590)
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.