modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755644479
|
hakimjustbao
| 2025-08-19T23:27:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:27:19Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo7_1
|
AnonymousCS
| 2025-08-19T23:22:26Z | 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
| 2025-08-19T23:19:38Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo7_1
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_combo7_1
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: 0.1934
- Accuracy: 0.9383
- 1-f1: 0.9062
- 1-recall: 0.8958
- 1-precision: 0.9170
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.276 | 1.0 | 25 | 0.1781 | 0.9486 | 0.9194 | 0.8803 | 0.9620 | 0.9315 |
| 0.1168 | 2.0 | 50 | 0.1891 | 0.9447 | 0.9138 | 0.8803 | 0.95 | 0.9286 |
| 0.156 | 3.0 | 75 | 0.1934 | 0.9383 | 0.9062 | 0.8958 | 0.9170 | 0.9276 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
unitova/blockassist-bc-zealous_sneaky_raven_1755643098
|
unitova
| 2025-08-19T23:09:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:09:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
torchao-testing/single-linear-Int4WeightOnlyConfig-preshuffled-v2-0.13-dev
|
torchao-testing
| 2025-08-19T23:08:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:07:13Z |
```
import torch
import io
model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda"))
from torchao.quantization import Int4WeightOnlyConfig, quantize_
quant_config = Int4WeightOnlyConfig(group_size=128, packing_format="preshuffled", version=2)
quantize_(model, quant_config)
example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),)
output = model(*example_inputs)
# Push to hub
USER_ID = "torchao-testing"
MODEL_NAME = "single-linear"
save_to = f"{USER_ID}/{MODEL_NAME}-Int4WeightOnlyConfig-preshuffled-v2-0.13.dev"
from huggingface_hub import HfApi
api = HfApi()
buf = io.BytesIO()
torch.save(model.state_dict(), buf)
api.create_repo(save_to, repo_type="model", exist_ok=True)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model.bin",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(example_inputs, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_inputs.pt",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(output, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_output.pt",
repo_id=save_to,
)
```
|
seraphimzzzz/44280
|
seraphimzzzz
| 2025-08-19T23:05:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:05:34Z |
[View on Civ Archive](https://civarchive.com/models/58312?modelVersionId=62763)
|
ultratopaz/64761
|
ultratopaz
| 2025-08-19T23:04:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:04:45Z |
[View on Civ Archive](https://civarchive.com/models/88038?modelVersionId=93695)
|
ultratopaz/52803
|
ultratopaz
| 2025-08-19T23:04:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:04:15Z |
[View on Civ Archive](https://civarchive.com/models/71849?modelVersionId=76589)
|
ultratopaz/49808
|
ultratopaz
| 2025-08-19T23:04:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:04:06Z |
[View on Civ Archive](https://civarchive.com/models/66953?modelVersionId=71614)
|
crystalline7/46077
|
crystalline7
| 2025-08-19T23:03:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:03:01Z |
[View on Civ Archive](https://civarchive.com/models/61266?modelVersionId=65736)
|
crystalline7/16814
|
crystalline7
| 2025-08-19T22:56:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:55Z |
[View on Civ Archive](https://civarchive.com/models/17067?modelVersionId=20153)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755642520
|
mang3dd
| 2025-08-19T22:55:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:54:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755642454
|
vwzyrraz7l
| 2025-08-19T22:53:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:53:37Z |
---
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).
|
seraphimzzzz/77178
|
seraphimzzzz
| 2025-08-19T22:53:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:53:33Z |
[View on Civ Archive](https://civarchive.com/models/23721?modelVersionId=109311)
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755642359
|
thanobidex
| 2025-08-19T22:51:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:51:45Z |
---
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).
|
neko-llm/Qwen3-235B-test5
|
neko-llm
| 2025-08-19T22:51:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:finetune:Qwen/Qwen3-235B-A22B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T12:49:08Z |
---
base_model: Qwen/Qwen3-235B-A22B
library_name: transformers
model_name: Qwen3-235B-test5
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-235B-test5
This model is a fine-tuned version of [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B).
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="neko-llm/Qwen3-235B-test5", 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/neko-llm/huggingface/runs/r6shuvcx)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.54.1
- Pytorch: 2.6.0
- 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}}
}
```
|
seraphimzzzz/75514
|
seraphimzzzz
| 2025-08-19T22:50:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:50:28Z |
[View on Civ Archive](https://civarchive.com/models/53478?modelVersionId=107222)
|
ultratopaz/24129
|
ultratopaz
| 2025-08-19T22:49:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:49:07Z |
[View on Civ Archive](https://civarchive.com/models/24156?modelVersionId=28870)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755643544
|
lilTAT
| 2025-08-19T22:46:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:46:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/66914
|
crystalline7
| 2025-08-19T22:46:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:46:09Z |
[View on Civ Archive](https://civarchive.com/models/90494?modelVersionId=96401)
|
crystalline7/9861
|
crystalline7
| 2025-08-19T22:45:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:45:35Z |
[View on Civ Archive](https://civarchive.com/models/8772?modelVersionId=10357)
|
crystalline7/185162
|
crystalline7
| 2025-08-19T22:45:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:45:12Z |
[View on Civ Archive](https://civarchive.com/models/212601?modelVersionId=239496)
|
crystalline7/55277
|
crystalline7
| 2025-08-19T22:43:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:43:29Z |
[View on Civ Archive](https://civarchive.com/models/75880?modelVersionId=80615)
|
crystalline7/96922
|
crystalline7
| 2025-08-19T22:42:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:42:14Z |
[View on Civ Archive](https://civarchive.com/models/122190?modelVersionId=133017)
|
ultratopaz/126381
|
ultratopaz
| 2025-08-19T22:42:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:42:02Z |
[View on Civ Archive](https://civarchive.com/models/60161?modelVersionId=166777)
|
seraphimzzzz/63725
|
seraphimzzzz
| 2025-08-19T22:41:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:41:25Z |
[View on Civ Archive](https://civarchive.com/models/86857?modelVersionId=92402)
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755641399
|
ihsanridzi
| 2025-08-19T22:35:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:35:48Z |
---
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).
|
EpistemeAI/gpt-oss-20b-unsloth-finetune-puzzle-lora-V3
|
EpistemeAI
| 2025-08-19T22:32:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T22:00:22Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755640573
|
vwzyrraz7l
| 2025-08-19T22:22:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:22:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/37221
|
crystalline7
| 2025-08-19T22:19:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:19:13Z |
[View on Civ Archive](https://civarchive.com/models/12757?modelVersionId=50853)
|
crystalline7/22906
|
crystalline7
| 2025-08-19T22:19:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:19:03Z |
[View on Civ Archive](https://civarchive.com/models/12757?modelVersionId=27712)
|
ultratopaz/37241
|
ultratopaz
| 2025-08-19T22:18:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:18:33Z |
[View on Civ Archive](https://civarchive.com/models/46276?modelVersionId=50887)
|
seraphimzzzz/83539
|
seraphimzzzz
| 2025-08-19T22:17:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:17:25Z |
[View on Civ Archive](https://civarchive.com/models/108640?modelVersionId=116962)
|
seraphimzzzz/50649
|
seraphimzzzz
| 2025-08-19T22:15:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:15:13Z |
[View on Civ Archive](https://civarchive.com/models/68312?modelVersionId=73002)
|
crystalline7/45885
|
crystalline7
| 2025-08-19T22:14:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:14:51Z |
[View on Civ Archive](https://civarchive.com/models/60936?modelVersionId=65415)
|
crystalline7/61249
|
crystalline7
| 2025-08-19T22:14:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:14:15Z |
[View on Civ Archive](https://civarchive.com/models/83928?modelVersionId=89196)
|
crystalline7/52079
|
crystalline7
| 2025-08-19T22:11:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:11:04Z |
[View on Civ Archive](https://civarchive.com/models/70744?modelVersionId=75429)
|
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)
|
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
|
ultratopaz/20170
|
ultratopaz
| 2025-08-19T22:03:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:03:23Z |
[View on Civ Archive](https://civarchive.com/models/20449?modelVersionId=24314)
|
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())
```
|
MattBou00/llama-3-2-1b-detox_v1b-checkpoint-epoch-60
|
MattBou00
| 2025-08-19T22:03:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-19T22:01:44Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_20-30-04/checkpoints/checkpoint-epoch-60")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
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")
```
|
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)
|
crystalline7/88579
|
crystalline7
| 2025-08-19T22:00:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:00:25Z |
[View on Civ Archive](https://civarchive.com/models/113817?modelVersionId=122997)
|
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
|
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)
|
ultratopaz/39149
|
ultratopaz
| 2025-08-19T21:59:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:59:02Z |
[View on Civ Archive](https://civarchive.com/models/49489?modelVersionId=54066)
|
ultratopaz/264895
|
ultratopaz
| 2025-08-19T21:56:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:56:10Z |
[View on Civ Archive](https://civarchive.com/models/297339?modelVersionId=334055)
|
crystalline7/65162
|
crystalline7
| 2025-08-19T21:55:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:55:37Z |
[View on Civ Archive](https://civarchive.com/models/88509?modelVersionId=94178)
|
ultratopaz/76136
|
ultratopaz
| 2025-08-19T21:55:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:55:24Z |
[View on Civ Archive](https://civarchive.com/models/100949?modelVersionId=108063)
|
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)
|
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).
|
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
|
crystalline7/61277
|
crystalline7
| 2025-08-19T21:48:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:56Z |
[View on Civ Archive](https://civarchive.com/models/83957?modelVersionId=89226)
|
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)
|
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)
|
seraphimzzzz/35923
|
seraphimzzzz
| 2025-08-19T21:48:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:48:02Z |
[View on Civ Archive](https://civarchive.com/models/44026?modelVersionId=48662)
|
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)
|
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755639865
|
Leoar
| 2025-08-19T21:46:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy toothy cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:46:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy toothy cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Clip-filtrado-de-Abigail-Lalama-y-Snayder/New-video-filtrado-de-Abigail-Lalama-y-Snayder.Viral.Video.Official.Tutorial
|
Clip-filtrado-de-Abigail-Lalama-y-Snayder
| 2025-08-19T21:45:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:45:19Z |
<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>
|
ultratopaz/12175
|
ultratopaz
| 2025-08-19T21:45:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:45:07Z |
[View on Civ Archive](https://civarchive.com/models/11722?modelVersionId=13849)
|
seraphimzzzz/70861
|
seraphimzzzz
| 2025-08-19T21:43:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:43:28Z |
[View on Civ Archive](https://civarchive.com/models/94981?modelVersionId=101324)
|
crystalline7/46922
|
crystalline7
| 2025-08-19T21:43:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:43:10Z |
[View on Civ Archive](https://civarchive.com/models/60932?modelVersionId=65410)
|
ultratopaz/116529
|
ultratopaz
| 2025-08-19T21:27:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:27:21Z |
[View on Civ Archive](https://civarchive.com/models/140376?modelVersionId=155559)
|
ultratopaz/35127
|
ultratopaz
| 2025-08-19T21:24:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:24:08Z |
[View on Civ Archive](https://civarchive.com/models/42534?modelVersionId=47223)
|
ultratopaz/645962
|
ultratopaz
| 2025-08-19T21:22:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:22:33Z |
[View on Civ Archive](https://civarchive.com/models/653843?modelVersionId=731791)
|
erikaputri-Viral-Video-Clip-XX-Link/Orginal.full.Videos.erika.putri.8.menit.viral.video.Official.Tutorial.telegram
|
erikaputri-Viral-Video-Clip-XX-Link
| 2025-08-19T21:21:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:21:48Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?crd
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
ultratopaz/45650
|
ultratopaz
| 2025-08-19T21:21:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:21:27Z |
[View on Civ Archive](https://civarchive.com/models/60581?modelVersionId=65049)
|
18-Archita-Phukan-Viral-video-original/New.full.videos.archita.phukan.Viral.Video.Official.Tutorial
|
18-Archita-Phukan-Viral-video-original
| 2025-08-19T21:20:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:20:43Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ultratopaz/39810
|
ultratopaz
| 2025-08-19T21:14:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:14:51Z |
[View on Civ Archive](https://civarchive.com/models/50818?modelVersionId=55334)
|
seraphimzzzz/343374
|
seraphimzzzz
| 2025-08-19T21:11:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:11:14Z |
[View on Civ Archive](https://civarchive.com/models/377663?modelVersionId=421726)
|
Muapi/3d-chibi-toy-air-dry-clay-style-flux
|
Muapi
| 2025-08-19T21:08:19Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:08:08Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 【3D chibi toy】Air dry clay style - FLUX
**Base model**: Flux.1 D
**Trained words**: Juaner_clay
## 🧠 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:689231@771373", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Abdullah6395/COT_LLM
|
Abdullah6395
| 2025-08-19T21:07:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:LiquidAI/LFM2-350M",
"base_model:adapter:LiquidAI/LFM2-350M",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T21:07:53Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/Screenshot from 2025-08-20 01-55-42.png
text: None
parameters:
negative_prompt: None
base_model: LiquidAI/LFM2-350M
instance_prompt: null
license: other
license_name: none
license_link: LICENSE
---
# CAYOTES
<Gallery />
## Model description
Model Description (Educational Purpose Only):
This is a small-scale LLM developed for learning and experimentation. Initially, the model was distilled from a larger teacher model to reduce size and computation requirements. Subsequently, it was fine-tuned on a chain-of-thought (CoT) dataset. Due to limited resources, training is partial and the model's outputs remain largely random. This model is intended strictly for educational use, research practice, and demonstration purposes. It is not suitable for deployment, commercial applications, or production use.
## Download model
[Download](/Abdullah6395/COT_LLM/tree/main) them in the Files & versions tab.
|
Muapi/richard-anderson
|
Muapi
| 2025-08-19T21:07:49Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T21:07:35Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Richard Anderson
**Base model**: Flux.1 D
**Trained words**: Art by Richard Anderson
## 🧠 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:1349128@1523853", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ultratopaz/64194
|
ultratopaz
| 2025-08-19T21:06:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:06:17Z |
[View on Civ Archive](https://civarchive.com/models/87387?modelVersionId=92998)
|
roeker/blockassist-bc-quick_wiry_owl_1755637429
|
roeker
| 2025-08-19T21:05:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T21:04:40Z |
---
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).
|
rayonlabs/tournament-tourn_e8b54a44823eb63b_20250819-f54191cf-9125-4bf8-bece-f68787965413-5FYeWKtZ
|
rayonlabs
| 2025-08-19T21:04:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"axolotl",
"grpo",
"unsloth",
"conversational",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T21:04:47Z |
---
library_name: transformers
model_name: app/checkpoints/f54191cf-9125-4bf8-bece-f68787965413/tournament-tourn_e8b54a44823eb63b_20250819-f54191cf-9125-4bf8-bece-f68787965413-5FYeWKtZ
tags:
- generated_from_trainer
- trl
- axolotl
- grpo
- unsloth
licence: license
---
# Model Card for app/checkpoints/f54191cf-9125-4bf8-bece-f68787965413/tournament-tourn_e8b54a44823eb63b_20250819-f54191cf-9125-4bf8-bece-f68787965413-5FYeWKtZ
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.1+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
matboz/ring-gemma-3
|
matboz
| 2025-08-19T21:04:28Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-3-27b-it",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-3-27b-it",
"region:us"
] |
text-generation
| 2025-08-19T21:04:07Z |
---
base_model: google/gemma-3-27b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-3-27b-it
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
ultratopaz/34402
|
ultratopaz
| 2025-08-19T21:01:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T21:01:47Z |
[View on Civ Archive](https://civarchive.com/models/40940?modelVersionId=46038)
|
Muapi/wizard-s-paper-model-universe
|
Muapi
| 2025-08-19T20:59:26Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T20:58:50Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Wizard's Paper Model Universe
**Base model**: Flux.1 D
**Trained words**: A paper model
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:873875@978295", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mradermacher/ege-8b-1.1-GGUF
|
mradermacher
| 2025-08-19T20:43:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"sft",
"unsloth",
"tr",
"dataset:orkungedik/function_call",
"base_model:orkungedik/ege-8b-1.1",
"base_model:quantized:orkungedik/ege-8b-1.1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:03:51Z |
---
base_model: orkungedik/ege-8b-1.1
datasets:
- orkungedik/function_call
language:
- tr
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- sft
- unsloth
---
## 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/orkungedik/ege-8b-1.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ege-8b-1.1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ege-8b-1.1-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/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ege-8b-1.1-GGUF/resolve/main/ege-8b-1.1.f16.gguf) | f16 | 16.5 | 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 -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755632515
|
lisaozill03
| 2025-08-19T20:06:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:06:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755632070
|
chainway9
| 2025-08-19T20:01:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T20:01:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cme1nlmc40afpgwtcpc42gvjm_cme7g43p30bf96aq1sh548pe8
|
BootesVoid
| 2025-08-19T18:26:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T18:26:12Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: OFMODEL
---
# Cme1Nlmc40Afpgwtcpc42Gvjm_Cme7G43P30Bf96Aq1Sh548Pe8
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `OFMODEL` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "OFMODEL",
"lora_weights": "https://huggingface.co/BootesVoid/cme1nlmc40afpgwtcpc42gvjm_cme7g43p30bf96aq1sh548pe8/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cme1nlmc40afpgwtcpc42gvjm_cme7g43p30bf96aq1sh548pe8', weight_name='lora.safetensors')
image = pipeline('OFMODEL').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cme1nlmc40afpgwtcpc42gvjm_cme7g43p30bf96aq1sh548pe8/discussions) to add images that show off what you’ve made with this LoRA.
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755627800
|
Vasya777
| 2025-08-19T18:23:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:23:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755626209
|
helmutsukocok
| 2025-08-19T18:22:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:22:05Z |
---
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).
|
MoLA-LLM/MoLA-v0.5-9x4b
|
MoLA-LLM
| 2025-08-19T18:12:06Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mola_lm",
"text-generation",
"pytorch",
"mixture-of-experts",
"lora",
"adapter",
"causal-lm",
"conversational",
"custom_code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-18T07:17:37Z |
---
license: apache-2.0
library_name: transformers
tags:
- pytorch
- mixture-of-experts
- lora
- adapter
- causal-lm
- text-generation
language:
- en
pipeline_tag: text-generation
---
**Important Note**: *This model has issues with the lora applying part of the custom lm class and its router is a bit too small with little generalization.
In v0.6 and future models, all of these issues are/will be resolved.*
**TLDR:** *Dont use this model, use v0.6 and above.*
# MoLA-LM: Mixture of LoRA Adapters LLM
MoLA-LM combines multiple LoRA adapters with an intelligent router to automatically select the best adapter for each input prompt. This approach enables specialized performance across different tasks while maintaining efficiency.
## Model Details
- **Model Type**: Mixture of LoRA Adapters Language Model
- **Base Model**: Qwen/Qwen3-4B-Thinking-2507
- **Total Adapters**: 9
- **Architecture**: Custom MoLAForCausalLM with automatic adapter routing
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model (trust_remote_code=True is required for custom architecture)
model = AutoModelForCausalLM.from_pretrained(
"MoLA-LLM/MoLA-v0.5-9x4b",
trust_remote_code=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("MoLA-LLM/MoLA-v0.5-9x4b", trust_remote_code=True)
# Use like any other language model - adapter selection is automatic
prompt = "Write a Python function to calculate fibonacci numbers"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=.6, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(f"Selected LoRA: {model.get_current_lora()}")
print(response)
```
*You can also use load_in_4bit and load_in_8bit directly when loading!*
## Architecture
The MoLA-LM architecture consists of:
1. **Base Model**: Qwen/Qwen3-4B-Thinking-2507
2. **Router Network**: Frozen encoder as Sentence transformer + decoder as one layer MLP for adapter selection
3. **LoRA Adapters**: 9 task-specific fine-tuned adapters
4. **Dynamic Switching**: Automatic adapter application based on input
---
## *Paper coming soon™*
|
janardhanb/qwen_2-5_coder_3b_text_to_cypher_2024v1
|
janardhanb
| 2025-08-19T18:10:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T18:06:53Z |
---
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]
|
koloni/blockassist-bc-deadly_graceful_stingray_1755625016
|
koloni
| 2025-08-19T18:03:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T18:03:30Z |
---
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).
|
MidnightRunner/MDNT_Illus_3D
|
MidnightRunner
| 2025-08-19T17:59:02Z | 0 | 0 |
diffusers
|
[
"diffusers",
"SDXL",
"mdnt-illus",
"3D-hybrid",
"anaglyph",
"photoreal",
"cinematic",
"text-to-image",
"ComfyUI",
"Automatic1111",
"en",
"base_model:MidnightRunner/MDNT_Illus",
"base_model:finetune:MidnightRunner/MDNT_Illus",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-08-19T14:50:48Z |
---
license: creativeml-openrail-m
language:
- en
base_model:
- OnomaAIResearch/Illustrious-XL-v2.0
- MidnightRunner/MDNT_Illus
tags:
- SDXL
- mdnt-illus
- 3D-hybrid
- anaglyph
- photoreal
- cinematic
- text-to-image
- ComfyUI
- Automatic1111
- diffusers
pipeline_tag: text-to-image
library_name: diffusers
metrics:
- FID
- IS
widget:
- text: >-
(masterpiece), extremely aesthetic, newest, very vibrant colors, (ultra-HD),
(cinematic lighting), (photorealistic), high detail, depth of field, best
quality, absurdres,
parameters:
negative_prompt: >-
bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra,
missing, worst quality, jpeg artifacts, bad quality, watermark,
unfinished, displeasing, sepia, sketch, flat color, signature, artistic
error, username, scan, (blurry, lowres, worst quality, (low quality:1.1),
ugly, (bad anatomy:1.05), artist name, (patreon username:1.2)
output:
url: mdnt_illus_3d_sample.jpeg
---
# MDNT_Illus_3D
Model type: diffusion-based text-to-image
Base model: Illustrious XL v2.0
Merged with: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova3D-CGXL, BetterDaysIllus
License: CreativeML Open RAIL++-M
## Model description
MDNT_Illus_3D is a precision finetune focused on a 3D-hybrid aesthetic, balancing photoreal fidelity with simulated depth and sculpted form. It emphasizes anaglyphic layering, volumetric lighting, cinematic depth of field, and richly detailed textures to produce imagery that feels tactile, immersive, and dramatically lit.
## Usage recommendations
### Sampling methods
- Euler A (Euler ancestral)
- DPM++ 2M Karras
- DPM++ 2M SDE Karras
- DPM++ 3M SDE Exponential
### Settings
- Steps: 25–45
- CFG scale: 4 (range 3–4)
- Clip skip: 1
### Workflow
Compatible with ComfyUI and Automatic1111. A tailored ComfyUI workflow may be added later to maximize spatial layering and volumetric light behavior.
## Prompt guidance
Positive (example)
```
realistic, photorealistic, very aesthetic, best quality, absurdres, masterpiece,
amazing quality, newest, scenery, depth of field, high-resolution, high definition,
visually intense anaglyphic experience, volumetric lighting, cinematic, sculpted 3D form
```
Negative (example)
```
bad hands, extra digits, (multiple views:1.1), (bad:1.05), fewer, extra, missing,
worst quality, jpeg artifacts, watermark, unfinished, sketch, flat color, signature,
artist name, blurry, lowres, (bad anatomy:1.05), (patreon username:1.2)
```
## Version changes / notes
- v1.0 (initial release)
- Introduces 3D-hybrid realism with anaglyphic depth and volumetric lighting
- Blended with Hyphorias, Nova 3DCG XL, BetterDaysIllus for expanded range
## Acknowledgments
Base: Illustrious XL v2.0
Merges: MIDNIGHT Illustrious, MDNT_Illus, Hyphorias, Nova 3DCG XL, BetterDaysIllus
## Additional Resources
- **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/)
## License
This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
|
AppliedLucent/nemo-phase6
|
AppliedLucent
| 2025-08-19T17:49:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:AppliedLucent/nemo-phase5",
"base_model:finetune:AppliedLucent/nemo-phase5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T17:38:35Z |
---
base_model: AppliedLucent/nemo-phase5
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AppliedLucent
- **License:** apache-2.0
- **Finetuned from model :** AppliedLucent/nemo-phase5
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
praveensonu/llama_unified_3b_instruct
|
praveensonu
| 2025-08-19T17:41:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T15:13:03Z |
---
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]
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755621443
|
kojeklollipop
| 2025-08-19T17:06:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:06:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EZCon/Qwen2.5-VL-7B-Instruct-4bit-mlx
|
EZCon
| 2025-08-19T17:05:03Z | 39 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"multimodal",
"unsloth",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-08-05T07:17:26Z |
---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- unsloth
- mlx
library_name: transformers
---
# EZCon/Qwen2.5-VL-7B-Instruct-4bit-mlx
This model was converted to MLX format from [`unsloth/Qwen2.5-VL-7B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2.5-VL-7B-Instruct-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755622837
|
Vasya777
| 2025-08-19T17:01:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:01:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
New-Clip-prabh-viral-videos/New.full.videos.prabh.Viral.Video.Official.Tutorial
|
New-Clip-prabh-viral-videos
| 2025-08-19T16:52:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T16:51:29Z |
[](https://tinyurl.com/bdk3zxvb)
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755620416
|
quantumxnode
| 2025-08-19T16:46:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:46:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755620279
|
sampingkaca72
| 2025-08-19T16:43:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:43:26Z |
---
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).
|
VER-milica-y-angel-david-debut-video/video.filtrado.milica.y.angel.david.debut.clip.viral.completo.en.twitter.y.telegram
|
VER-milica-y-angel-david-debut-video
| 2025-08-19T16:40:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T16:39:51Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
haji80mr-uoft/semi-wotype-Llama-tuned-Lora-merged-V0
|
haji80mr-uoft
| 2025-08-19T16:18:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T16:16:18Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** haji80mr-uoft
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
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.