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 |
|---|---|---|---|---|---|---|---|---|---|
motza0025/blockassist-bc-ferocious_territorial_chinchilla_1756026335
|
motza0025
| 2025-08-24T09:21:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious territorial chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T09:20:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious territorial chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kavpro/blockassist-bc-tall_lively_caribou_1756026598
|
kavpro
| 2025-08-24T09:11:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T09:10:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thuhien140386/blockassist-bc-screeching_running_owl_1756025876
|
thuhien140386
| 2025-08-24T09:07:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching running owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T09:07:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching running owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
edimaosom1/blockassist-bc-padded_crested_gull_1756024812
|
edimaosom1
| 2025-08-24T09:06:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded crested gull",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T09:06:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded crested gull
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1756024486
|
indoempatnol
| 2025-08-24T09:04:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T09:04:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1756025673
|
lqpl
| 2025-08-24T08:56:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:55:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aiomatic/ceousman
|
aiomatic
| 2025-08-24T08:55:45Z | 27 | 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
| 2025-08-16T17:15:15Z |
---
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: ceousman
---
# Ceousman
<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 `ceousman` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ceousman",
"lora_weights": "https://huggingface.co/aiomatic/ceousman/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('aiomatic/ceousman', weight_name='lora.safetensors')
image = pipeline('ceousman').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: 3000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/aiomatic/ceousman/discussions) to add images that show off what you’ve made with this LoRA.
|
douhu881a/blockassist-bc-leaping_rangy_yak_1756024498
|
douhu881a
| 2025-08-24T08:35:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leaping rangy yak",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:35:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leaping rangy yak
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joanna302/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
|
joanna302
| 2025-08-24T08:34:07Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T08:22:48Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base).
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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_1_part_SFT_2e-05/runs/z5mltwit)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 3.6.0
- 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}}
}
```
|
douhu881a/blockassist-bc-leaping_rangy_yak_1756024383
|
douhu881a
| 2025-08-24T08:33:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leaping rangy yak",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:33:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leaping rangy yak
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756022650
|
mang3dd
| 2025-08-24T08:29:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:29:44Z |
---
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).
|
Alex1281/blockassist-bc-stealthy_stinging_alligator_1756020204
|
Alex1281
| 2025-08-24T08:13:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy stinging alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:13:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy stinging alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
csukuangfj/vits-piper-pl_PL-zenski_wg_glos-medium
|
csukuangfj
| 2025-08-24T08:06:26Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-07-16T02:45:46Z |
List of several Polish voice models for piper
===
All models were trained on the RTX4090 graphics card. Datasets for the indicated models can be found in another repository. 1600-2000 samples were used to generate the models. Generated sample texts read by the included models are also included.
How to use models?
---
`pip install piper-tts`
`echo 'Witamy w świecie syntezy mowy!' | piper --model ./pl_PL-jarvis_wg_glos-medium.onnx --config ./pl_PL-jarvis_wg_glos-medium.onnx.json --output_file witaj.wav`
How to use models in MacOS:
---
```
pip install piper-phonemize-cross
pip install piper-tts --no-deps
pip install onnxruntime
```
`echo 'Witamy w świecie syntezy mowy!' | piper --model ./pl_PL-meski_wg_glos-medium.onnx --config ./pl_PL-meski_wg_glos-medium.onnx.json --output_file witaj.wav`
Info
---
All models was tuning from file `epoch=2164-step=1355540.ckpt` (https://huggingface.co/datasets/rhasspy/piper-checkpoints/resolve/main/en/en_US/lessac/medium/epoch%3D2164-step%3D1355540.ckpt) and tuning ware taken around 10h per voice.
pl_PL-jarvis_wg_glos-medium: `epoch=2499-step=1395740.ckpt`
pl_PL-justyna_wg_glos-medium: `epoch=2499-step=1387030.ckpt`
pl_PL-meski_wg_glos-medium: `epoch=4449-step=1593180.ckpt`
pl_PL-zenski_wg_glos-medium: `epoch=4949-step=1645180.ckpt`
---
license: mit
---
|
csukuangfj/vits-piper-pl_PL-justyna_wg_glos-medium
|
csukuangfj
| 2025-08-24T08:06:14Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-07-16T02:45:03Z |
List of several Polish voice models for piper
===
All models were trained on the RTX4090 graphics card. Datasets for the indicated models can be found in another repository. 1600-2000 samples were used to generate the models. Generated sample texts read by the included models are also included.
How to use models?
---
`pip install piper-tts`
`echo 'Witamy w świecie syntezy mowy!' | piper --model ./pl_PL-jarvis_wg_glos-medium.onnx --config ./pl_PL-jarvis_wg_glos-medium.onnx.json --output_file witaj.wav`
How to use models in MacOS:
---
```
pip install piper-phonemize-cross
pip install piper-tts --no-deps
pip install onnxruntime
```
`echo 'Witamy w świecie syntezy mowy!' | piper --model ./pl_PL-meski_wg_glos-medium.onnx --config ./pl_PL-meski_wg_glos-medium.onnx.json --output_file witaj.wav`
Info
---
All models was tuning from file `epoch=2164-step=1355540.ckpt` (https://huggingface.co/datasets/rhasspy/piper-checkpoints/resolve/main/en/en_US/lessac/medium/epoch%3D2164-step%3D1355540.ckpt) and tuning ware taken around 10h per voice.
pl_PL-jarvis_wg_glos-medium: `epoch=2499-step=1395740.ckpt`
pl_PL-justyna_wg_glos-medium: `epoch=2499-step=1387030.ckpt`
pl_PL-meski_wg_glos-medium: `epoch=4449-step=1593180.ckpt`
pl_PL-zenski_wg_glos-medium: `epoch=4949-step=1645180.ckpt`
---
license: mit
---
|
unitova/blockassist-bc-zealous_sneaky_raven_1756020821
|
unitova
| 2025-08-24T08:01:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T08:01:24Z |
---
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).
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1756019651
|
rafsya427
| 2025-08-24T07:40:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:40:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
imge/reinforce_llama_v1.7
|
imge
| 2025-08-24T07:38:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T17:29:42Z |
---
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]
|
rambetiko/blockassist-bc-soft_lanky_marmot_1756020581
|
rambetiko
| 2025-08-24T07:36:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft lanky marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:35:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft lanky marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kimweb3/blockassist-bc-camouflaged_sedate_pheasant_1756020743
|
kimweb3
| 2025-08-24T07:33:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged sedate pheasant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:33:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged sedate pheasant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nkimaj-29/swin-tiny-patch4-window7-224-finetuned-eurosat
|
Nkimaj-29
| 2025-08-24T07:21:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-24T06:55:11Z |
---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0505
- Accuracy: 0.9833
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.196 | 1.0 | 190 | 0.0840 | 0.9737 |
| 0.1049 | 2.0 | 380 | 0.0624 | 0.9807 |
| 0.1085 | 3.0 | 570 | 0.0505 | 0.9833 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
koloni/blockassist-bc-deadly_graceful_stingray_1756018337
|
koloni
| 2025-08-24T07:20:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:20:00Z |
---
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).
|
Belliseso/blockassist-bc-hunting_marine_owl_1756018423
|
Belliseso
| 2025-08-24T07:18:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hunting marine owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:18:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hunting marine owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756018077
|
kojeklollipop
| 2025-08-24T07:14:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T07:14:13Z |
---
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).
|
ggmancer/blockassist-bc-reclusive_keen_marmot_1756017541
|
ggmancer
| 2025-08-24T06:59:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive keen marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:59:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive keen marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Andyen512/DDHpose
|
Andyen512
| 2025-08-24T06:49:40Z | 0 | 1 | null |
[
"diffusion",
"3D human pose estimation",
"arxiv:2403.04444",
"license:mit",
"region:us"
] | null | 2025-08-24T02:19:23Z |
---
license: mit
tags:
- diffusion
- 3D human pose estimation
paper: https://arxiv.org/abs/2403.04444
---
# DDHPose: Disentangled Diffusion-Based 3D Human Pose Estimation
Official repo for the paper:
**[Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser](https://arxiv.org/abs/2403.04444)**
Authors: Qingyuan Cai, Xuecai Hu, Saihui Hou, Li Yao, Yongzhen Huang
Conference: AAAI 2024
## Dependencies
Make sure you have the following dependencies installed (python):
* pytorch >= 0.4.0
* matplotlib=3.1.0
* einops
* timm
* tensorboard
You should download [MATLAB](https://www.mathworks.com/products/matlab-online.html) if you want to evaluate our model on MPI-INF-3DHP dataset.
## Datasets
Our model is evaluated on [Human3.6M](http://vision.imar.ro/human3.6m) and [MPI-INF-3DHP](https://vcai.mpi-inf.mpg.de/3dhp-dataset/) datasets.
### Human3.6M
We set up the Human3.6M dataset in the same way as [VideoPose3D](https://github.com/facebookresearch/VideoPose3D/blob/master/DATASETS.md). You can download the processed data from [here](https://drive.google.com/file/d/1FMgAf_I04GlweHMfgUKzB0CMwglxuwPe/view?usp=sharing). `data_2d_h36m_gt.npz` is the ground truth of 2D keypoints. `data_2d_h36m_cpn_ft_h36m_dbb.npz` is the 2D keypoints obatined by [CPN](https://github.com/GengDavid/pytorch-cpn). `data_3d_h36m.npz` is the ground truth of 3D human joints. Put them in the `./data` directory.
### MPI-INF-3DHP
We set up the MPI-INF-3DHP dataset following [D3DP](https://github.com/paTRICK-swk/D3DP). You can download the processed data from [here](https://drive.google.com/file/d/1zOM_CvLr4Ngv6Cupz1H-tt1A6bQPd_yg/view?usp=share_link). Put them in the `./data` directory.
## Evaluating our models
You can download our pre-trained models, which are evaluated on Human3.6M (from [here](https://drive.google.com/drive/folders/1P9zbC_VMw_1K4DTTFFglLSN2J1PoI5kd?usp=sharing)) and MPI-INF-3DHP (from [here](https://drive.google.com/drive/folders/1yux7QiLOpHqJXVB9GaVz5A279JunGfuX?usp=sharing)). Put them in the `./checkpoint` directory.
### Human3.6M
To evaluate our D3DP with JPMA using the 2D keypoints obtained by CPN as inputs, please run:
```bash
python main.py -k cpn_ft_h36m_dbb -c checkpoint/best_h36m_model -gpu 0 --evaluate best_epoch.bin -num_proposals 1 -sampling_timesteps 1 -b 4 --p2
```
to compare with the deterministic methods.
Please run:
```bash
python main.py -k cpn_ft_h36m_dbb -c checkpoint/best_h36m_model -gpu 0 --evaluate best_epoch.bin -num_proposals 20 -sampling_timesteps 10 -b 4 --p2
```
to compare with the probabilistic methods.
You can balance efficiency and accuracy by adjusting `-num_proposals` (number of hypotheses) and `-sampling_timesteps` (number of iterations).
### MPI-INF-3DHP
To evaluate our D3DP with JPMA using the ground truth 2D poses as inputs, please run:
```bash
python main_3dhp.py -c checkpoint/best_3dhp_model -gpu 0 --evaluate best_epoch.bin -num_proposals 5 -sampling_timesteps 5 -b 4 --p2
```
After that, the predicted 3D poses under P-Best, P-Agg, J-Best, J-Agg settings are saved as four files (`.mat`) in `./checkpoint`. To get the MPJPE, AUC, PCK metrics, you can evaluate the predictions by running a Matlab script `./3dhp_test/test_util/mpii_test_predictions_ori_py.m` (you can change 'aggregation_mode' in line 29 to get results under different settings). Then, the evaluation results are saved in `./3dhp_test/test_util/mpii_3dhp_evaluation_sequencewise_ori_{setting name}_t{iteration index}.csv`. You can manually average the three metrics in these files over six sequences to get the final results. An example is shown in `./3dhp_test/test_util/H20_K10/mpii_3dhp_evaluation_sequencewise_ori_J_Best_t10.csv`.
## Quickstart
```bash
pip install -U "huggingface_hub[cli]" torch
# 下载权重（示例）
hf snapshot download Andyen512/DDHpose -r main -p checkpoints/
## Training from scratch
### Human3.6M
To train our model using the 2D keypoints obtained by CPN as inputs, please run:
```bash
python main.py -k cpn_ft_h36m_dbb -c checkpoint/model_ddhpose_h36m -gpu 0
```
### MPI-INF-3DHP
To train our model using the ground truth 2D poses as inputs, please run:
```bash
python main_3dhp.py -c checkpoint/model_ddhpose_3dhp -gpu 0
```
## **Citation**
```bibtex
@inproceedings{cai2024disentangled,
title={Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser},
author={Cai, Qingyuan and Hu, Xuecai and Hou, Saihui and Yao, Li and Huang, Yongzhen},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={2},
pages={882--890},
year={2024}
}
```
## Acknowledgement
Our code refers to the following repositories.
* [VideoPose3D](https://github.com/facebookresearch/VideoPose3D)
* [MixSTE](https://github.com/JinluZhang1126/MixSTE)
* [D3DP](https://github.com/paTRICK-swk/D3DP)
We thank the authors for releasing their codes
|
Hjambatukam/blockassist-bc-silent_bellowing_boar_1756018121
|
Hjambatukam
| 2025-08-24T06:49:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent bellowing boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:49:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent bellowing boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_0.0002
|
joanna302
| 2025-08-24T06:41:56Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T19:25:29Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_0.0002
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_0.0002
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base).
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="joanna302/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_0.0002", 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/prism-eval/Qwen3-8B-Base_pag_mt_alpaca_0.33_part_SFT_0.0002/runs/g967zbsc)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 3.6.0
- 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}}
}
```
|
uoppou/blockassist-bc-curious_wild_rooster_1756017680
|
uoppou
| 2025-08-24T06:41:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious wild rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:41:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious wild rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
uoppou/blockassist-bc-skittish_beaked_duck_1756017146
|
uoppou
| 2025-08-24T06:32:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish beaked duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:32:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish beaked duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-noisy_elusive_grouse_1756016543
|
AnerYubo
| 2025-08-24T06:22:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"noisy elusive grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:22:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- noisy elusive grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
node89/blockassist-bc-untamed_tough_hawk_1756015634
|
node89
| 2025-08-24T06:09:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed tough hawk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T06:08:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed tough hawk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
edimaosom1/blockassist-bc-padded_crested_gull_1756013282
|
edimaosom1
| 2025-08-24T05:57:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded crested gull",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T05:57:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded crested gull
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nema122/blockassist-bc-robust_fluffy_ram_1756012517
|
nema122
| 2025-08-24T05:16:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T05:16:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SREELU1308/Llama3.2_fintuned_merged
|
SREELU1308
| 2025-08-24T05:06:51Z | 5 | 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
| 2025-08-20T10:39:07Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** SREELU1308
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-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)
|
suiseihoshimachi/blockassist-bc-mighty_deadly_ibis_1756010949
|
suiseihoshimachi
| 2025-08-24T04:49:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mighty deadly ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T04:49:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mighty deadly ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
suiseihoshimachi/blockassist-bc-mighty_deadly_ibis_1756010366
|
suiseihoshimachi
| 2025-08-24T04:40:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mighty deadly ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T04:39:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mighty deadly ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SwetaJena/llama-3.2-1B-elephant_numbers_student_12_v2
|
SwetaJena
| 2025-08-24T04:22:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T04:22:30Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SwetaJena
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
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)
|
bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF
|
bartowski
| 2025-08-24T03:57:50Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:CrucibleLab/M3.2-24B-Loki-V1.3",
"base_model:quantized:CrucibleLab/M3.2-24B-Loki-V1.3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-23T20:08:40Z |
---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: CrucibleLab/M3.2-24B-Loki-V1.3
base_model_relation: quantized
---
## Llamacpp imatrix Quantizations of M3.2-24B-Loki-V1.3 by CrucibleLab
Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6258">b6258</a> for quantization.
Original model: https://huggingface.co/CrucibleLab/M3.2-24B-Loki-V1.3
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) combined with a subset of combined_all_small.parquet from Ed Addario [here](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_all_small.parquet)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggml-org/llama.cpp), or any other llama.cpp based project
## Prompt format
No prompt format found, check original model page
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [M3.2-24B-Loki-V1.3-bf16.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-bf16.gguf) | bf16 | 47.15GB | false | Full BF16 weights. |
| [M3.2-24B-Loki-V1.3-Q8_0.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q8_0.gguf) | Q8_0 | 25.05GB | false | Extremely high quality, generally unneeded but max available quant. |
| [M3.2-24B-Loki-V1.3-Q6_K_L.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q6_K_L.gguf) | Q6_K_L | 19.67GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q6_K.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q6_K.gguf) | Q6_K | 19.35GB | false | Very high quality, near perfect, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q5_K_L.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q5_K_L.gguf) | Q5_K_L | 17.18GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q5_K_M.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q5_K_M.gguf) | Q5_K_M | 16.76GB | false | High quality, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q5_K_S.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q5_K_S.gguf) | Q5_K_S | 16.30GB | false | High quality, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q4_1.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q4_1.gguf) | Q4_1 | 14.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [M3.2-24B-Loki-V1.3-Q4_K_L.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q4_K_L.gguf) | Q4_K_L | 14.83GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q4_K_M.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q4_K_M.gguf) | Q4_K_M | 14.33GB | false | Good quality, default size for most use cases, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q4_K_S.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q4_K_S.gguf) | Q4_K_S | 13.55GB | false | Slightly lower quality with more space savings, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q4_0.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q4_0.gguf) | Q4_0 | 13.49GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [M3.2-24B-Loki-V1.3-IQ4_NL.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ4_NL.gguf) | IQ4_NL | 13.47GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [M3.2-24B-Loki-V1.3-Q3_K_XL.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q3_K_XL.gguf) | Q3_K_XL | 12.99GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [M3.2-24B-Loki-V1.3-IQ4_XS.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ4_XS.gguf) | IQ4_XS | 12.76GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [M3.2-24B-Loki-V1.3-Q3_K_L.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q3_K_L.gguf) | Q3_K_L | 12.40GB | false | Lower quality but usable, good for low RAM availability. |
| [M3.2-24B-Loki-V1.3-Q3_K_M.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q3_K_M.gguf) | Q3_K_M | 11.47GB | false | Low quality. |
| [M3.2-24B-Loki-V1.3-IQ3_M.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ3_M.gguf) | IQ3_M | 10.65GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [M3.2-24B-Loki-V1.3-Q3_K_S.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q3_K_S.gguf) | Q3_K_S | 10.40GB | false | Low quality, not recommended. |
| [M3.2-24B-Loki-V1.3-IQ3_XS.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ3_XS.gguf) | IQ3_XS | 9.91GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [M3.2-24B-Loki-V1.3-Q2_K_L.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q2_K_L.gguf) | Q2_K_L | 9.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [M3.2-24B-Loki-V1.3-IQ3_XXS.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ3_XXS.gguf) | IQ3_XXS | 9.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [M3.2-24B-Loki-V1.3-Q2_K.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-Q2_K.gguf) | Q2_K | 8.89GB | false | Very low quality but surprisingly usable. |
| [M3.2-24B-Loki-V1.3-IQ2_M.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ2_M.gguf) | IQ2_M | 8.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [M3.2-24B-Loki-V1.3-IQ2_S.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ2_S.gguf) | IQ2_S | 7.48GB | false | Low quality, uses SOTA techniques to be usable. |
| [M3.2-24B-Loki-V1.3-IQ2_XS.gguf](https://huggingface.co/bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF/blob/main/CrucibleLab_M3.2-24B-Loki-V1.3-IQ2_XS.gguf) | IQ2_XS | 7.21GB | false | Low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF --include "CrucibleLab_M3.2-24B-Loki-V1.3-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/CrucibleLab_M3.2-24B-Loki-V1.3-GGUF --include "CrucibleLab_M3.2-24B-Loki-V1.3-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (CrucibleLab_M3.2-24B-Loki-V1.3-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggml-org/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggml-org/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggml-org/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1756003908
|
rafsya427
| 2025-08-24T03:18:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T03:18:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WenFengg/swing27_14_31_2
|
WenFengg
| 2025-08-24T03:11:06Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-07-31T02:31:00Z |
---
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]
|
PhongInk/blockassist-bc-stinky_thorny_zebra_1756003774
|
PhongInk
| 2025-08-24T02:50:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky thorny zebra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:49:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky thorny zebra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756002751
|
eshanroy5678
| 2025-08-24T02:39:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:36:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
PhongInk/blockassist-bc-stinky_thorny_zebra_1756002860
|
PhongInk
| 2025-08-24T02:35:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky thorny zebra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:34:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky thorny zebra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Clip-Uppal-Farm-Girl-Video-Viral-Original/Full.Uppal.Farm.Girl.Viral.Video.Original.Link.Official
|
Clip-Uppal-Farm-Girl-Video-Viral-Original
| 2025-08-24T02:24:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T02:23:15Z |
18 seconds ago
<a href="https://tinyurl.com/52jc3rtk" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tinyurl.com/52jc3rtk" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=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>
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|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755995595
|
pempekmangedd
| 2025-08-24T00:57:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:57:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mikadat/blockassist-bc-gregarious_ferocious_aardvark_1755996575
|
mikadat
| 2025-08-24T00:50:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious ferocious aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:49:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious ferocious aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1755995255
|
pidbu
| 2025-08-24T00:29:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:28:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
urewstok223/blockassist-bc-squeaky_territorial_stingray_1755994551
|
urewstok223
| 2025-08-24T00:16:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squeaky territorial stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:16:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squeaky territorial stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755992928
|
ihsanridzi
| 2025-08-24T00:15:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:15:16Z |
---
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).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755991686
|
indoempatnol
| 2025-08-23T23:54:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T23:54:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DiegoDKz/woman_kissing_breast2
|
DiegoDKz
| 2025-08-23T23:42:34Z | 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
| 2025-08-23T23:24:34Z |
---
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: kissing breast
---
# Woman_Kissing_Breast2
<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 `kissing breast` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "kissing breast",
"lora_weights": "https://huggingface.co/DiegoDKz/woman_kissing_breast2/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('DiegoDKz/woman_kissing_breast2', weight_name='lora.safetensors')
image = pipeline('kissing breast').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: 12
## Contribute your own examples
You can use the [community tab](https://huggingface.co/DiegoDKz/woman_kissing_breast2/discussions) to add images that show off what you’ve made with this LoRA.
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755989160
|
mang3dd
| 2025-08-23T23:12:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T23:12:15Z |
---
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).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755988586
|
Sayemahsjn
| 2025-08-23T22:55:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T22:55:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bigdefence/Bigvox-HyperCLOVAX-Audio
|
bigdefence
| 2025-08-23T22:02:45Z | 32 | 1 | null |
[
"safetensors",
"omni_speech_HyperCLOVAX",
"speech-to-text",
"korean",
"llama",
"audio",
"voice",
"bigdefence",
"HyperCLOVAX",
"naver",
"audio-text-to-text",
"ko",
"base_model:naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
"base_model:finetune:naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
"license:apache-2.0",
"region:us"
] |
audio-text-to-text
| 2025-07-21T09:37:30Z |
---
license: apache-2.0
language:
- ko
base_model:
- naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B
tags:
- speech-to-text
- korean
- llama
- audio
- voice
- bigdefence
- HyperCLOVAX
- naver
pipeline_tag: audio-text-to-text
---
## 🎧 Bigvox
- **Bigvox**은 한국어 음성 인식에 특화된 고성능, 저지연 음성 언어 멀티모달 모델입니다. [naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B](https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B) 기반으로 구축되었습니다. 🚀
- **End-to-End** 음성 멀티모달 구조를 채택하여 음성 입력부터 텍스트 출력까지 하나의 파이프라인에서 처리하며, 추가적인 중간 모델 없이 자연스럽게 멀티모달 처리를 지원합니다.
### 📂 모델 접근
- **GitHub**: [bigdefence/bigvox-hyperclovax](https://github.com/bigdefence/bigvox-hyperclovax) 🌐
- **HuggingFace**: [bigdefence/Bigvox-HyperCLOVAX-Audio](https://huggingface.co/bigdefence/Bigvox-HyperCLOVAX-Audio) 🤗
- **모델 크기**: 1B 파라미터 📊
## 🌟 주요 특징
- **🇰🇷 한국어 특화**: 한국어 음성 패턴과 언어적 특성에 최적화
- **⚡ 경량화**: 1B 파라미터로 효율적인 추론 성능
- **🎯 고정확도**: 다양한 한국어 음성 환경에서 우수한 성능
- **🔧 실용성**: 실시간 음성 인식 애플리케이션에 적합
## 📋 모델 정보
| 항목 | 세부사항 |
|------|----------|
| **기반 모델** | naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B |
| **언어** | 한국어 (Korean) |
| **모델 크기** | ~1B 파라미터 |
| **작업 유형** | Speech-to-Text 음성 멀티모달 |
| **라이선스** | Apache 2.0 |
### 🔧 레포지토리 다운로드 및 환경 설정
**Bigvox**을 시작하려면 다음과 같이 레포지토리를 클론하고 환경을 설정하세요. 🛠️
1. **레포지토리 클론**:
```bash
git clone https://github.com/bigdefence/bigvox-hyperclovax
cd bigvox-hyperclovax
```
2. **의존성 설치**:
```bash
bash setting.sh
```
### 📥 다운로드 방법
**Huggingface CLI 사용**:
```bash
pip install -U huggingface_hub
huggingface-cli download bigdefence/Bigvox-HyperCLOVAX-Audio --local-dir ./checkpoints
```
**Snapshot Download 사용**:
```bash
pip install -U huggingface_hub
```
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="bigdefence/Bigvox-HyperCLOVAX-Audio",
local_dir="./checkpoints",
resume_download=True
)
```
**Git 사용**:
```bash
git lfs install
git clone https://huggingface.co/bigdefence/Bigvox-HyperCLOVAX-Audio
```
### 🛠️ 의존성 모델
- **Speech Encoder**: [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) 🎤
### 🔄 로컬 추론
**Bigvox**으로 추론을 수행하려면 다음 단계를 따라 모델을 설정하고 로컬에서 실행하세요. 📡
1. **모델 준비**:
- [HuggingFace](https://huggingface.co/bigdefence/Bigvox-HyperCLOVAX-Audio)에서 **Bigvox** 다운로드 📦
- [HuggingFace](https://huggingface.co/openai/whisper-large-v3)에서 **Whisper-large-v3** 음성 인코더를 다운로드하여 `./models/speech_encoder/` 디렉토리에 배치 🎤
2. **추론 실행**:
- **음성-텍스트(S2T)** 추론:
- **Non-Streaming**
```bash
python3 omni_speech/infer/bigvox.py --query_audio test_audio.wav
```
- **Streaming**
```bash
python3 omni_speech/infer/bigvox_streaming.py --query_audio test_audio.wav
```
## 🔧 훈련 세부사항
### 훈련 설정
- **Base Model**: naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B
- **Hardware**: 1x NVIDIA RTX 6000A GPU
- **Training Time**: 3시간
## ⚠️ 제한사항
- 배경 소음이 심한 환경에서는 성능이 저하될 수 있습니다
- 매우 빠른 발화나 중얼거리는 말투에 대해서는 인식률이 떨어질 수 있습니다
- 전문 용어나 고유명사에 대한 인식률은 도메인에 따라 차이가 있을 수 있습니다
## 📜 라이선스
이 모델은 Apache 2.0 라이선스 하에 배포됩니다. 상업적 사용이 가능하며, 자세한 내용은 [LICENSE](LICENSE) 파일을 참조하세요.
## 📞 문의사항
- **개발**: BigDefence
## 📈 업데이트 로그
### v1.0.0 (2024.12)
- 🎉 **초기 모델 릴리즈**: Bigvox 공개
- 🇰🇷 **한국어 특화**: HyperCLOVAX-SEED-Text-Instruct-0.5B 기반 한국어 음성-텍스트 음성 멀티모달 모델
---
## 🤝 기여하기
**Bigvox** 프로젝트에 기여하고 싶으시다면:
---
**BigDefence**와 함께 한국어 AI 음성 인식의 미래를 만들어가세요! 🚀🇰🇷
*"Every voice matters, every word counts - 모든 목소리가 중요하고, 모든 말이 가치 있습니다"*
|
vennertou/blockassist-bc-freckled_amphibious_dove_1755985736
|
vennertou
| 2025-08-23T21:49:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"freckled amphibious dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T21:48:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- freckled amphibious dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sabhaaa/blockassist-bc-nimble_sedate_cheetah_1755982286
|
sabhaaa
| 2025-08-23T20:52:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nimble sedate cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:52:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nimble sedate cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755981634
|
0xaoyama
| 2025-08-23T20:40:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:40:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755979987
|
unitova
| 2025-08-23T20:38:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:38:32Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755981159
|
roeker
| 2025-08-23T20:33:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:33:19Z |
---
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).
|
mahmoudOmar03/writing_task2_with_scores
|
mahmoudOmar03
| 2025-08-23T20:27:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T20:26:51Z |
---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mahmoudOmar03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
roeker/blockassist-bc-quick_wiry_owl_1755980781
|
roeker
| 2025-08-23T20:27:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:26:57Z |
---
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).
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755979315
|
0xaoyama
| 2025-08-23T20:02:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T20:02:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MowVNB/blockassist-bc-feline_grazing_macaw_1755977955
|
MowVNB
| 2025-08-23T19:57:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feline grazing macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T19:57:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feline grazing macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755978768
|
kapalbalap
| 2025-08-23T19:53:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T19:53:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ankurmishraa/bhasha-ai-en-as
|
ankurmishraa
| 2025-08-23T19:42:07Z | 0 | 0 | null |
[
"safetensors",
"mt5",
"license:apache-2.0",
"region:us"
] | null | 2025-08-23T19:38:29Z |
---
license: apache-2.0
---
|
mooperyou/blockassist-bc-stinky_webbed_gecko_1755975961
|
mooperyou
| 2025-08-23T19:06:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky webbed gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T19:06:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky webbed gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
praise1214/blockassist-bc-sharp_ferocious_buffalo_1755971273
|
praise1214
| 2025-08-23T18:30:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sharp ferocious buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T18:29:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sharp ferocious buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sukuna4sol/blockassist-bc-pensive_elusive_caribou_1755973586
|
sukuna4sol
| 2025-08-23T18:28:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive elusive caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T18:28:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive elusive caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1755973366
|
kayacrypto
| 2025-08-23T18:24:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T18:24:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755971307
|
ihsanridzi
| 2025-08-23T18:14:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T18:14:40Z |
---
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).
|
kavpro/blockassist-bc-tall_lively_caribou_1755971736
|
kavpro
| 2025-08-23T17:56:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T17:56:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DopeorNope/cpt_fft_3200
|
DopeorNope
| 2025-08-23T17:53:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-23T17:48:14Z |
---
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]
|
saikatsantra/farming-lama-lora-finetune
|
saikatsantra
| 2025-08-23T17:51:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T17:50:31Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** saikatsantra
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
links-uppal-farm-girl-original-viral-video/New.full.videos.uppal.farm.girl.Viral.Video.Official.Tutorial
|
links-uppal-farm-girl-original-viral-video
| 2025-08-23T17:43:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-23T17:43:07Z |
<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=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>
|
dgambettaphd/M_llm3_run2_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-08-23T17:31:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T17:31:12Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **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]
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755964823
|
lqpl
| 2025-08-23T16:01:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T16:01:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vikky7864/blockassist-bc-mimic_sniffing_mole_1755963982
|
vikky7864
| 2025-08-23T15:47:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mimic sniffing mole",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T15:47:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mimic sniffing mole
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755961034
|
ihsanridzi
| 2025-08-23T15:24:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T15:24:24Z |
---
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).
|
RikiyaT/mxbai-ettin-68m-reddit-phaseB_1200
|
RikiyaT
| 2025-08-23T15:20:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-23T15:20:04Z |
---
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]
|
Abhiram1009/Qwen3-14B-ft-4bit
|
Abhiram1009
| 2025-08-23T14:28:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-22T09:11:24Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **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]
|
VIDEOS-18-Zeenat-Viral-Video-Clip-XX/New.full.videos.zeenat.Viral.Video.Official.Tutorial
|
VIDEOS-18-Zeenat-Viral-Video-Clip-XX
| 2025-08-23T14:21:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-23T14:21:46Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1755958597
|
kittygirlhere
| 2025-08-23T14:17:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T14:17:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755952933
|
ihsanridzi
| 2025-08-23T13:09:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T13:09:02Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755952566
|
liukevin666
| 2025-08-23T12:37:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T12:37:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mhurhangee/roberta-base-ep-claims
|
mhurhangee
| 2025-08-23T12:20:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-23T12:20:47Z |
---
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]
|
windecay/SimpleSDXL2
|
windecay
| 2025-08-23T11:46:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-09T17:37:20Z |
---
license: apache-2.0
---
|
kayacrypto/blockassist-bc-thriving_barky_wolf_1755948598
|
kayacrypto
| 2025-08-23T11:32:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving barky wolf",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T11:32:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving barky wolf
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dombili2038/blockassist-bc-jumping_beaked_hamster_1755948674
|
Dombili2038
| 2025-08-23T11:31:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping beaked hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T11:31:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping beaked hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WenFengg/a_14l29_238
|
WenFengg
| 2025-08-23T10:28:40Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-23T10:26:21Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
kejuss/blockassist-bc-timid_voracious_gecko_1755944795
|
kejuss
| 2025-08-23T10:27:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid voracious gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T10:26:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid voracious gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755943345
|
liukevin666
| 2025-08-23T10:04:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T10:03:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nema122/blockassist-bc-robust_fluffy_ram_1755941441
|
nema122
| 2025-08-23T09:31:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T09:31:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-noisy_elusive_grouse_1755940453
|
AnerYubo
| 2025-08-23T09:14:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"noisy elusive grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T09:14:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- noisy elusive grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChenWu98/statement_deepseek_v1.5_sft_cluster_weighted_split_0
|
ChenWu98
| 2025-08-23T07:56:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT",
"base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T07:47:24Z |
---
base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT
library_name: transformers
model_name: statement_deepseek_v1.5_sft_cluster_weighted_split_0
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for statement_deepseek_v1.5_sft_cluster_weighted_split_0
This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT).
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
[<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/chenwu/huggingface/runs/o2aqnf1o)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- 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}}
}
```
|
lautan/blockassist-bc-gentle_patterned_goat_1755931551
|
lautan
| 2025-08-23T07:12:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T07:12:07Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755930125
|
roeker
| 2025-08-23T06:23:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T06:22:48Z |
---
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).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755927903
|
kojeklollipop
| 2025-08-23T06:16:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T06:16:54Z |
---
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).
|
Mostefa-Terbeche/diabetic-retinopathy-aptos-efficientnet_b3-gentle-20250724-104646
|
Mostefa-Terbeche
| 2025-08-23T05:29:36Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:aptos",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-23T05:08:18Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- aptos
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: aptos_efficientnet_b3_gentle
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: aptos
name: APTOS
metrics:
- type: accuracy
value: 0.7868852459016393
- type: quadratic-kappa
value: 0.8957074261994582
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the efficientnet_b3 architecture on the aptos dataset with gentle preprocessing.
## Model Details
- **Architecture**: efficientnet_b3
- **Dataset**: aptos
- **Preprocessing**: gentle
- **Training Date**: 20250724-104646
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: aptos_efficientnet_b3_20250724-104646_new
## Performance
- **Test Accuracy**: 0.7868852459016393
- **Test Quadratic Kappa**: 0.8957074261994582
- **Validation Kappa**: 0.8957074261994582
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-aptos-efficientnet_b3-gentle",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
roeker/blockassist-bc-quick_wiry_owl_1755923189
|
roeker
| 2025-08-23T04:27:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-23T04:27:11Z |
---
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).
|
NaderAli/lama_fp32.onnx
|
NaderAli
| 2025-08-23T03:20:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-23T03:20:19Z |
---
license: apache-2.0
---
|
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