modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 00:38:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 525
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-29 00:38:28
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
chainway9/blockassist-bc-untamed_quick_eel_1756334551
|
chainway9
| 2025-08-27T23:13:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T23:13:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
uzakihana286/blockassist-bc-sly_armored_barracuda_1756336372
|
uzakihana286
| 2025-08-27T23:13:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly armored barracuda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T23:13:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly armored barracuda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756336203
|
ggozzy
| 2025-08-27T23:11:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T23:11:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xuandin/Qwen-4B-Thinking-2507-SFT-NumQA-new
|
xuandin
| 2025-08-27T23:09:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-4B-Thinking-2507",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-4B-Thinking-2507",
"region:us"
] |
text-generation
| 2025-08-27T18:16:49Z |
---
base_model: Qwen/Qwen3-4B-Thinking-2507
library_name: peft
model_name: Qwen-4B-Thinking-2507-SFT-NumQA-new
tags:
- base_model:adapter:Qwen/Qwen3-4B-Thinking-2507
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for Qwen-4B-Thinking-2507-SFT-NumQA-new
This model is a fine-tuned version of [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- PEFT 0.17.1
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.7.1
- 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}}
}
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756335955
|
Dejiat
| 2025-08-27T23:06:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T23:06:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kandedarrag/kande
|
kandedarrag
| 2025-08-27T22:58:49Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-27T22:58:49Z |
---
license: apache-2.0
---
|
matboz/gemma-2-9b-it-risk-rank1-19-93.61
|
matboz
| 2025-08-27T22:58:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2-9b-it",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-2-9b-it",
"region:us"
] |
text-generation
| 2025-08-27T22:57:13Z |
---
base_model: google/gemma-2-9b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-2-9b-it
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
ncgiron/barberia
|
ncgiron
| 2025-08-27T22:58:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-22T21:21:21Z |
# example
---
license: mit
---
test
|
ypszn/blockassist-bc-yapping_pawing_worm_1756335105
|
ypszn
| 2025-08-27T22:52:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:52:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
frifruktik5/blockassist-bc-nasty_domestic_raven_1756335048
|
frifruktik5
| 2025-08-27T22:51:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nasty domestic raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:51:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nasty domestic raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ypwang61/One-Shot-RLVR-R1-Distill-1.5B-1.2k-dsr-sub
|
ypwang61
| 2025-08-27T22:47:52Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:ypwang61/One-Shot-RLVR-Datasets",
"arxiv:2504.20571",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T18:41:26Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-Math-1.5B
datasets:
- ypwang61/One-Shot-RLVR-Datasets
---
This repository contains the model presented in [Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://huggingface.co/papers/2504.20571).
Code: https://github.com/ypwang61/One-Shot-RLVR
|
ypwang61/One-Shot-RLVR-R1-Distill-1.5B-4-shot
|
ypwang61
| 2025-08-27T22:47:32Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:ypwang61/One-Shot-RLVR-Datasets",
"arxiv:2504.20571",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T18:51:38Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-Math-1.5B
datasets:
- ypwang61/One-Shot-RLVR-Datasets
---
This repository contains the model presented in [Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://huggingface.co/papers/2504.20571).
Code: https://github.com/ypwang61/One-Shot-RLVR
|
ypwang61/One-Shot-RLVR-R1-Distill-1.5B-pi1
|
ypwang61
| 2025-08-27T22:47:21Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:ypwang61/One-Shot-RLVR-Datasets",
"arxiv:2504.20571",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T18:42:17Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-Math-1.5B
datasets:
- ypwang61/One-Shot-RLVR-Datasets
---
This repository contains the model presented in [Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://huggingface.co/papers/2504.20571).
Code: https://github.com/ypwang61/One-Shot-RLVR
|
ypwang61/One-Shot-RLVR-Qwen2.5-Math-7B-1.2k-dsr-sub
|
ypwang61
| 2025-08-27T22:46:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"dataset:ypwang61/One-Shot-RLVR-Datasets",
"arxiv:2504.20571",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T21:56:47Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-Math-1.5B
datasets:
- ypwang61/One-Shot-RLVR-Datasets
---
This repository contains the model presented in [Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://huggingface.co/papers/2504.20571).
Code: https://github.com/ypwang61/One-Shot-RLVR
|
bah63843/blockassist-bc-plump_fast_antelope_1756334615
|
bah63843
| 2025-08-27T22:44:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:44:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756333068
|
hakimjustbao
| 2025-08-27T22:43:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:43:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756333098
|
koloni
| 2025-08-27T22:43:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:43:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756334488
|
Dejiat
| 2025-08-27T22:41:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:41:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_010
|
AnonymousCS
| 2025-08-27T22:40:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T22:39:08Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_010
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. -->
# populism_classifier_bsample_010
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8224
- Accuracy: 0.8347
- 1-f1: 0.2174
- 1-recall: 0.8
- 1-precision: 0.1258
- Balanced Acc: 0.8178
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0876 | 1.0 | 9 | 0.9320 | 0.6774 | 0.1511 | 1.0 | 0.0817 | 0.8339 |
| 0.0168 | 2.0 | 18 | 0.7873 | 0.7532 | 0.1762 | 0.92 | 0.0975 | 0.8341 |
| 0.0227 | 3.0 | 27 | 0.5728 | 0.8668 | 0.2468 | 0.76 | 0.1473 | 0.8150 |
| 0.0047 | 4.0 | 36 | 0.5762 | 0.8772 | 0.2621 | 0.76 | 0.1583 | 0.8203 |
| 0.0039 | 5.0 | 45 | 0.8224 | 0.8347 | 0.2174 | 0.8 | 0.1258 | 0.8178 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
AnonymousCS/populism_classifier_bsample_008
|
AnonymousCS
| 2025-08-27T22:37:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T22:37:01Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_008
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. -->
# populism_classifier_bsample_008
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7890
- Accuracy: 0.75
- 1-f1: 0.3101
- 1-recall: 1.0
- 1-precision: 0.1835
- Balanced Acc: 0.8676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0387 | 1.0 | 6 | 0.4787 | 0.8146 | 0.3654 | 0.95 | 0.2262 | 0.8783 |
| 0.0256 | 2.0 | 12 | 0.6968 | 0.75 | 0.3101 | 1.0 | 0.1835 | 0.8676 |
| 0.0289 | 3.0 | 18 | 0.4770 | 0.8343 | 0.3789 | 0.9 | 0.24 | 0.8652 |
| 0.0067 | 4.0 | 24 | 0.7193 | 0.7725 | 0.3306 | 1.0 | 0.1980 | 0.8795 |
| 0.0109 | 5.0 | 30 | 0.7890 | 0.75 | 0.3101 | 1.0 | 0.1835 | 0.8676 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
ypszn/blockassist-bc-yapping_pawing_worm_1756334170
|
ypszn
| 2025-08-27T22:37:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:36:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756334040
|
Dejiat
| 2025-08-27T22:34:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:34:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756333961
|
ggozzy
| 2025-08-27T22:33:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:33:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
geoplus/task-14-Qwen-Qwen2.5-3B-Instruct
|
geoplus
| 2025-08-27T22:33:14Z | 26 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-08-12T23:21:54Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
Xenova/LaMini-Flan-T5-783M
|
Xenova
| 2025-08-27T22:32:15Z | 972 | 27 |
transformers.js
|
[
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"base_model:MBZUAI/LaMini-Flan-T5-783M",
"base_model:quantized:MBZUAI/LaMini-Flan-T5-783M",
"region:us"
] | null | 2023-05-03T14:08:44Z |
---
base_model: MBZUAI/LaMini-Flan-T5-783M
library_name: transformers.js
---
https://huggingface.co/MBZUAI/LaMini-Flan-T5-783M with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Text-to-text generation.
```js
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
const output = await generator('how can I become more healthy?', {
max_new_tokens: 100,
});
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
Xenova/LaMini-T5-61M
|
Xenova
| 2025-08-27T22:31:46Z | 183 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"base_model:MBZUAI/LaMini-T5-61M",
"base_model:quantized:MBZUAI/LaMini-T5-61M",
"region:us"
] | null | 2023-05-03T14:46:00Z |
---
base_model: MBZUAI/LaMini-T5-61M
library_name: transformers.js
---
https://huggingface.co/MBZUAI/LaMini-T5-61M with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Text-to-text generation.
```js
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text2text-generation', 'Xenova/LaMini-T5-61M');
const output = await generator('how can I become more healthy?', {
max_new_tokens: 100,
});
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
Xenova/tiny-random-WhisperForConditionalGeneration_timestamped
|
Xenova
| 2025-08-27T22:30:20Z | 8 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"region:us"
] |
automatic-speech-recognition
| 2024-06-29T22:05:06Z |
---
tags:
- transformers.js
---
Code to generate:
```py
from transformers import WhisperForConditionalGeneration, AutoProcessor
new_config_values = dict(
d_model = 16,
decoder_attention_heads = 4,
decoder_layers = 1,
encoder_attention_heads = 4,
encoder_layers = 1,
num_hidden_layers = 1,
ignore_mismatched_sizes=True,
)
original_model = WhisperForConditionalGeneration.from_pretrained('openai/whisper-tiny', **new_config_values)
original_model.save_pretrained('converted')
original_processor = AutoProcessor.from_pretrained('openai/whisper-tiny')
original_processor.save_pretrained('converted')
```
Followed by:
```sh
$ mkdir -p ./converted/onnx
$ optimum-cli export onnx -m ./converted ./converted/onnx --task automatic-speech-recognition-with-past
$ find ./converted/onnx -type f ! -name "*.onnx" -delete
```
|
Xenova/nb-whisper-medium-beta
|
Xenova
| 2025-08-27T22:29:27Z | 16 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:NbAiLab/nb-whisper-medium-beta",
"base_model:quantized:NbAiLab/nb-whisper-medium-beta",
"region:us"
] |
automatic-speech-recognition
| 2023-08-29T00:24:06Z |
---
base_model: NbAiLab/nb-whisper-medium-beta
library_name: transformers.js
---
https://huggingface.co/NbAiLab/nb-whisper-medium-beta with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Transcribe audio from a URL.
```js
import { pipeline } from '@huggingface/transformers';
const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/nb-whisper-medium-beta');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url);
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
Xenova/flan-t5-base
|
Xenova
| 2025-08-27T22:28:36Z | 152 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"base_model:google/flan-t5-base",
"base_model:quantized:google/flan-t5-base",
"region:us"
] | null | 2023-05-03T20:14:52Z |
---
base_model: google/flan-t5-base
library_name: transformers.js
---
https://huggingface.co/google/flan-t5-base with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Text-to-text generation.
```js
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text2text-generation', 'Xenova/flan-t5-base');
const output = await generator('how can I become more healthy?', {
max_new_tokens: 100,
});
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
baseten/gemma-3-27b-causallm-it
|
baseten
| 2025-08-27T22:25:52Z | 0 | 0 | null |
[
"safetensors",
"gemma3_text",
"region:us"
] | null | 2025-08-27T22:05:13Z |
```
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-27b-it", torch_dtype="bfloat16")
model.language_model.push_to_hub("baseten/gemma-3-27b-causallm-it")
```
|
bigdefence/Midm-2.0-Mini-Vision-Instruct
|
bigdefence
| 2025-08-27T22:19:25Z | 2 | 0 | null |
[
"safetensors",
"llava_midm",
"image-to-text",
"korean",
"image",
"VLM",
"bigdefence",
"midm",
"KT",
"K-intelligence",
"ko",
"base_model:K-intelligence/Midm-2.0-Mini-Instruct",
"base_model:finetune:K-intelligence/Midm-2.0-Mini-Instruct",
"license:apache-2.0",
"region:us"
] |
image-to-text
| 2025-08-27T04:10:26Z |
---
license: apache-2.0
language:
- ko
base_model:
- K-intelligence/Midm-2.0-Mini-Instruct
tags:
- image-to-text
- korean
- image
- VLM
- bigdefence
- midm
- KT
- K-intelligence
pipeline_tag: image-to-text
---
## 📊 Midm-2.0-Mini-Vision-Instruct
- **Midm-2.0-Mini-Vision-Instruct**은 Midm-2.0-Mini-Vision-Instruct은 한국어 이미지 인식에 특화된 고성능, 경량 Vision-Language Model입니다. K-intelligence/Midm-2.0-Mini-Instruct 기반으로 구축되어 한국어 텍스트가 포함된 이미지 이해와 한국어 응답 생성에 최적화되었습니다.
- **End-to-End** LLaVA 구조를 채택하여 이미지 입력부터 텍스트 출력까지 하나의 파이프라인에서 처리하며, 추가적인 중간 모델 없이 자연스럽게 멀티모달 처리를 지원합니다.
### 📂 모델 접근
- **GitHub**: [bigdefence/midm-vision](https://github.com/bigdefence/midm-vision) 🌐
- **HuggingFace**: [bigdefence/Midm-2.0-Mini-Vision-Instruct](https://huggingface.co/bigdefence/Midm-2.0-Mini-Vision-Instruct) 🤗
- **모델 크기**: 2B 파라미터 📊
## 🌟 주요 특징
- **🇰🇷 한국어 특화**: 한국어 음성 패턴과 언어적 특성에 최적화
- **⚡ 경량화**: 2B 파라미터로 효율적인 추론 성능
- **🎯 고정확도**: 다양한 한국어 음성 환경에서 우수한 성능
- **🔧 실용성**: 실시간 음성 인식 애플리케이션에 적합
## 📋 모델 정보
| 항목 | 세부사항 |
|------|----------|
| **기반 모델** | K-intelligence/Midm-2.0-Mini-Instruct |
| **언어** | 한국어 (Korean) |
| **모델 크기** | ~2B 파라미터 |
| **작업 유형** | Image-to-Text 이미지 멀티모달 |
| **라이선스** | Apache 2.0 |
### 🔧 레포지토리 다운로드 및 환경 설정
**Midm-2.0-Mini-Vision-Instruct**을 시작하려면 다음과 같이 레포지토리를 클론하고 환경을 설정하세요. 🛠️
1. **레포지토리 클론**:
```bash
git clone https://github.com/bigdefence/midm-vision
cd midm-vision
```
2. **의존성 설치**:
```bash
conda create -n midm-vision python=3.10 -y
conda activate midm-vision
pip install -e .
```
### 📥 다운로드 방법
**Huggingface CLI 사용**:
```bash
pip install -U huggingface_hub
huggingface-cli download bigdefence/Midm-Vision --local-dir ./checkpoints
```
**Snapshot Download 사용**:
```bash
pip install -U huggingface_hub
```
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="bigdefence/Midm-Vision",
local_dir="./checkpoints",
resume_download=True
)
```
**Git 사용**:
```bash
git lfs install
git clone https://huggingface.co/bigdefence/midm-vision
```
### 🔄 로컬 추론
**Midm-Vision**으로 추론을 수행하려면 다음 단계를 따라 모델을 설정하고 로컬에서 실행하세요. 📡
1. **모델 준비**:
- [HuggingFace](https://huggingface.co/bigdefence/Midm-2.0-Mini-Vision-Instruct)에서 **Midm-2.0-Mini-Vision-Instruct** 다운로드 📦
2. **추론 실행**:
- **Streaming**
```bash
python3 infer.py --model-path checkpoints --image-file test.jpg
```
## 🔧 훈련 세부사항
### 훈련 설정
- **Base Model**: K-intelligence/Midm-2.0-Mini-Instruct
- **Hardware**: 4x NVIDIA RTX 4090 GPU
- **Training Time**: 10시간
## 📜 라이선스
이 모델은 Apache 2.0 라이선스 하에 배포됩니다. 상업적 사용이 가능하며, 자세한 내용은 [LICENSE](LICENSE) 파일을 참조하세요.
## 📞 문의사항
- **개발**: BigDefence
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756332965
|
ggozzy
| 2025-08-27T22:17:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:17:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756332932
|
Dejiat
| 2025-08-27T22:15:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:15:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jhtar/vit-beans-cmu-lab
|
jhtar
| 2025-08-27T22:14:55Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-27T00:17:26Z |
---
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]
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[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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756332715
|
ggozzy
| 2025-08-27T22:13:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:13:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756332628
|
bah63843
| 2025-08-27T22:11:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:11:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_155
|
AnonymousCS
| 2025-08-27T22:11:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_cased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_cased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T22:10:15Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_cased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_155
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. -->
# populism_classifier_bsample_155
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_cased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_cased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7143
- Accuracy: 0.9008
- 1-f1: 0.4248
- 1-recall: 0.6486
- 1-precision: 0.3158
- Balanced Acc: 0.7823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3495 | 1.0 | 9 | 0.7115 | 0.8214 | 0.3464 | 0.8378 | 0.2183 | 0.8291 |
| 0.0867 | 2.0 | 18 | 0.8843 | 0.7664 | 0.3014 | 0.8919 | 0.1813 | 0.8254 |
| 0.0133 | 3.0 | 27 | 0.6226 | 0.8443 | 0.3704 | 0.8108 | 0.24 | 0.8285 |
| 0.0069 | 4.0 | 36 | 0.7904 | 0.7939 | 0.3284 | 0.8919 | 0.2012 | 0.8400 |
| 0.0861 | 5.0 | 45 | 0.7143 | 0.9008 | 0.4248 | 0.6486 | 0.3158 | 0.7823 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
abartupsadernal/blockassist-bc-tawny_thorny_quail_1756332288
|
abartupsadernal
| 2025-08-27T22:05:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tawny thorny quail",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:05:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tawny thorny quail
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756332178
|
Dejiat
| 2025-08-27T22:03:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T22:03:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_144
|
AnonymousCS
| 2025-08-27T21:58:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_cased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_cased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T21:57:33Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_cased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_144
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. -->
# populism_classifier_bsample_144
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_cased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_cased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4677
- Accuracy: 0.9054
- 1-f1: 0.3521
- 1-recall: 0.9615
- 1-precision: 0.2155
- Balanced Acc: 0.9327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3191 | 1.0 | 19 | 0.5477 | 0.8818 | 0.3114 | 1.0 | 0.1844 | 0.9393 |
| 0.0338 | 2.0 | 38 | 0.3926 | 0.9096 | 0.3577 | 0.9423 | 0.2207 | 0.9255 |
| 0.0037 | 3.0 | 57 | 0.3975 | 0.9111 | 0.3663 | 0.9615 | 0.2262 | 0.9356 |
| 0.002 | 4.0 | 76 | 0.4677 | 0.9054 | 0.3521 | 0.9615 | 0.2155 | 0.9327 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-0.25-v2_8550
|
luckeciano
| 2025-08-27T21:58:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T17:42:54Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-0.25-v2_8550
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-0.25-v2_8550
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-0.25-v2_8550", 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/max-ent-llms/PolicyGradientStability/runs/5hmj947c)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jinmchoi/vit-beans-cmu-lab
|
jinmchoi
| 2025-08-27T21:57:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-27T21:56:42Z |
---
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]
|
mradermacher/Canum-med-Qwen3-Reasoning-GGUF
|
mradermacher
| 2025-08-27T21:56:55Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"text-generation-inference",
"medical",
"article",
"moe",
"biology",
"en",
"dataset:mteb/raw_medrxiv",
"base_model:prithivMLmods/Canum-med-Qwen3-Reasoning",
"base_model:quantized:prithivMLmods/Canum-med-Qwen3-Reasoning",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-27T17:04:10Z |
---
base_model: prithivMLmods/Canum-med-Qwen3-Reasoning
datasets:
- mteb/raw_medrxiv
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- text-generation-inference
- medical
- article
- moe
- biology
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Canum-med-Qwen3-Reasoning-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Canum-med-Qwen3-Reasoning-GGUF/resolve/main/Canum-med-Qwen3-Reasoning.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756331716
|
liukevin666
| 2025-08-27T21:56:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:56:18Z |
---
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).
|
liensonnguyenhoang/InterleavedThinking-3B-lora
|
liensonnguyenhoang
| 2025-08-27T21:54:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T07:22:44Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** liensonnguyenhoang
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
motza0025/blockassist-bc-nocturnal_long_leopard_1756329976
|
motza0025
| 2025-08-27T21:53:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nocturnal long leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:53:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nocturnal long leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fasdertaw/blockassist-bc-horned_barky_cheetah_1756331324
|
fasdertaw
| 2025-08-27T21:49:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"horned barky cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:49:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- horned barky cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_133
|
AnonymousCS
| 2025-08-27T21:45:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_cased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_cased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T21:44:16Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_cased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_133
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. -->
# populism_classifier_bsample_133
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_cased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_cased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5165
- Accuracy: 0.7949
- 1-f1: 0.4286
- 1-recall: 0.9
- 1-precision: 0.2812
- Balanced Acc: 0.8425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0571 | 1.0 | 6 | 0.6901 | 0.7037 | 0.3580 | 0.9667 | 0.2197 | 0.8229 |
| 0.5124 | 2.0 | 12 | 0.5041 | 0.8575 | 0.4792 | 0.7667 | 0.3485 | 0.8164 |
| 0.1476 | 3.0 | 18 | 0.4954 | 0.8148 | 0.4538 | 0.9 | 0.3034 | 0.8534 |
| 0.0133 | 4.0 | 24 | 0.5836 | 0.7749 | 0.4234 | 0.9667 | 0.2710 | 0.8618 |
| 0.0078 | 5.0 | 30 | 0.5165 | 0.7949 | 0.4286 | 0.9 | 0.2812 | 0.8425 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
wilfredomartel/bge-m3-es-legal-v5
|
wilfredomartel
| 2025-08-27T21:44:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:7872",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"es",
"dataset:wilfredomartel/small-spanish-legal-dataset",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-27T21:44:07Z |
---
language:
- es
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:7872
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: La Corte Constitucional inadmitió a trámite la acción extraordinaria
de protección N°. 3030-18-EP, presentada por Luis Alberto Bermeo Molina, debido
a que el auto de llamamiento a juicio no constituye un auto definitivo. Según
el artículo 58 de la Ley Orgánica de Garantías Jurisdiccionales y Control Constitucional,
esta acción procede únicamente sobre sentencias y autos definitivos. El Tribunal
de la Sala de Admisión, en su resolución del 27 de junio de 2019, consideró que
el auto de llamamiento a juicio, si bien pone fin a una etapa procesal, no decide
sobre el fondo del proceso penal ni produce efectos irrevocables, ya que los supuestos
de hecho y derecho pueden ser desvirtuados en etapas posteriores. El artículo
608, numeral 5 del Código Orgánico Integral Penal (COIP) establece que las declaraciones
contenidas en el auto de llamamiento a juicio no surten efectos irrevocables.
Por consiguiente, al no ser un auto definitivo, la Corte Constitucional no es
competente para tramitar esta acción, ya que no cumple con el objeto de la garantía
jurisdiccional.
sentences:
- ¿Cuál fue la razón principal para que la Corte Constitucional inadmitiera la acción
extraordinaria de protección del IESS en el caso 1009-19-EP, basándose en el artículo
62, numeral 3 de la LOGJCC, que prohíbe basar la acción únicamente en la consideración
de lo injusto de la sentencia?
- ¿En qué fecha y ante qué instancia se presentó la acción extraordinaria de protección
N°. 1311-19-EP, y cuál fue la pretensión concreta de la accionante?
- ¿Cuál fue la razón principal por la que la Corte Constitucional inadmitió a trámite
la acción extraordinaria de protección N°. 3030-18-EP, presentada por Luis Alberto
Bermeo Molina contra el auto de llamamiento a juicio?
- source_sentence: La Sala de Admisión inadmitió la acción extraordinaria de protección
N°. 0609-19-EP porque el accionante basó su argumentación fundamentalmente en
la supuesta no aplicación o errónea aplicación de la Ley Orgánica del Sistema
de Contratación Pública (LOSNCP) en relación con el derecho a impugnar una multa.
La Corte determinó que tales cuestiones, relativas a la correcta interpretación
y aplicación de legislación ordinaria, corresponden exclusivamente a la jurisdicción
ordinaria. El reclamo del accionante sobre la omisión de notificación de la resolución
de multa y la consiguiente violación de su derecho a impugnar, conforme al Artículo
103 de la LOSNCP, fue considerado un asunto de interpretación legal ordinaria
y no una violación directa de derechos constitucionales que amerite revisión constitucional
mediante acción extraordinaria de protección. La Corte citó el Artículo 62, numeral
4 de la Ley Orgánica de Garantías Jurisdiccionales y Control Constitucional (LOGJCC),
que prohíbe fundar la acción en la falta de aplicación o errónea aplicación de
la ley, enfatizando que su omisión desnaturaliza la acción extraordinaria de protección.
sentences:
- ¿Por qué la Sala de Admisión de la Corte Constitucional inadmitió la acción extraordinaria
de protección N°. 0609-19-EP, presentada por Fernando Leopoldo Pérez contra el
GAD de Tungurahua, respecto a una multa impuesta en una obra vial?
- ¿Cuáles fueron los argumentos de la accionante, Directora de Asesoría Jurídica
del Ministerio de Trabajo, para solicitar la acción extraordinaria de protección
contra la sentencia de la Corte Provincial de Pichincha en el caso 1846-19-EP?
- ¿En qué fecha la Sala de Admisión de la Corte Constitucional avocó conocimiento
de la causa No. 2362-18-EP y quiénes conformaron el Tribunal ponente?
- source_sentence: La señora Rosa Elvira Pérez Maldonado, en su acción extraordinaria
de protección No. 0525-10-EP, fundamentó su reclamo en la presunta vulneración
de sus derechos al debido proceso y a la debida motivación. Según el texto, «La
recurrente, sostiene que el fallo objetado, vulnera los derechos al debido proceso
y a la debida motivación que debe poseer toda resolución proveniente del poder
público, contemplado en el artículo 76, número 7, letra 1) de la Constitución
de la República, toda vez que los Jueces demandados en su sentencia no han tomado
en cuenta que existía una verdadera relación laboral entre el IESS y la accionante,
relación que fue reconocida con la certificación otorgada por la señora Delegada
de Recursos Humanos del IESS de la provincia de El Oro (a fojas 33), en la que
se le encargó las funciones de Subdirectora Regional Administrativa, sin que dicho
encargo, le obligue a renunciar a sus derechos como empleada de carrera (ingresó
en el año 1974), sujeta a las normas del Código del Trabajo». La accionante argumentó
que la sentencia emitida el 23 de marzo de 2010 por la Segunda Sala de lo Laboral
de la Corte Nacional de Justicia incurrió en una falta de motivación al no tomar
en cuenta este elemento fáctico y jurídico crucial, y que la sentencia debió haber
considerado esta realidad para resolver el juicio verbal sumario signado con el
número 0307-2008.
sentences:
- ¿Cuáles fueron las razones específicas por las cuales la Corte Constitucional
inadmitió a trámite la acción extraordinaria de protección N° 1169-19-EP presentada
por el Banco Comercial de Manabí S.A. contra la sentencia de la Corte Nacional
de Justicia?
- ¿Qué normativa constitucional y legal fundamentó la pretensión de Rene Nicanor
Crespo Campoverde al presentar la acción extraordinaria de protección N° 3056-18-EP
y qué artículo del Código Orgánico de la Función Judicial consideró vulnerado?
- ¿En qué consistió la alegada vulneración de los derechos al debido proceso y a
la debida motivación por parte de la Segunda Sala de lo Laboral de la Corte Nacional
de Justicia, según la acción extraordinaria de protección No. 0525-10-EP presentada
por Rosa Elvira Pérez Maldonado?
- source_sentence: La Corte Constitucional inadmitió la acción extraordinaria de protección
No. 0132-2010-EP debido a que la demanda no cumplió con los requisitos de admisibilidad
establecidos en los numerales 1 al 6 del artículo 62 de la Ley Orgánica de Garantías
Jurisdiccionales y Control Constitucional (LOGJCC). Específicamente, la Sala de
Admisión determinó que la providencia impugnada, dictada por el Juzgado Segundo
de Tránsito de Los Ríos el 31 de julio de 2009, no ameritaba un análisis de fondo
por parte de la Corte Constitucional, ya que implicaría someter a debate constitucional
aspectos ya tratados en el proceso original. Adicionalmente, se observó que la
acción fue presentada el 07 de diciembre de 2009, lo cual excede el término legal
establecido en el Art. 60 de la LOGJCC, que fija un plazo para la interposición
de este tipo de acciones.
sentences:
- ¿Por qué la Corte Constitucional inadmitió la acción extraordinaria de protección
No. 0132-2010-EP presentada por Elvia María Guevara Torre y otros contra una providencia
del Juzgado Segundo de Tránsito de Los Ríos?
- ¿En qué casos la Corte Constitucional puede conocer una acción extraordinaria
de protección contra sentencias o autos definitivos en Ecuador, según la normativa
transitoria?
- ¿Cuál fue el argumento central de la acción extraordinaria de protección Nro.
2621-18-EP respecto a la supuesta indebida aplicación de normas de coactiva por
parte de la Sala Penal de la Corte Provincial de Pichincha?
- source_sentence: La Sala de Admisión de la Corte Constitucional inadmitió la acción
extraordinaria de protección interpuesta por el Ec. Guillermo Antonio Quezada
Terán, representante legal de TRIPLEORO CEM., debido a una serie de deficiencias
sustanciales y formales identificadas en su petición. En primer lugar, la Sala
determinó que el accionante no logró justificar la relevancia constitucional del
conflicto planteado. El fundamento de su acción se centró en evidenciar lo que
consideraba improcedente y equivocado de la sentencia de la Segunda Sala de lo
Laboral de la Corte Nacional de Justicia, argumentando que se atentaba contra
los intereses económicos de su representada y del cantón Machala al imponer el
pago de sumas que, a su juicio, no correspondían legal ni justamente. Este tipo
de argumentación, centrada en la legalidad y los intereses económicos, no es el
objeto principal de la acción extraordinaria de protección, la cual está diseñada
para salvaguardar derechos constitucionales y el debido proceso, no para reexaminar
la correcta aplicación de leyes ordinarias o la valoración de pruebas desde una
perspectiva económica. Adicionalmente, la Sala constató una falta de precisión
en la identificación del acto materia de impugnación. Si bien la demanda inicialmente
se dirigía contra la sentencia de 30 de noviembre de 2009, emitida dentro del
recurso de casación No. 128-2009, en la sección de pretensiones se solicitaba
la nulidad de todo lo actuado desde la sentencia dictada por el Juez Primero Ocasional
del Trabajo de El Oro, dentro del juicio laboral No. 16-2006. Esta imprecisión
genera incertidumbre sobre el alcance de la impugnación y el acto específico que
supuestamente vulneró derechos constitucionales, dificultando el análisis de procedibilidad.
sentences:
- ¿Cuál fue el motivo principal por el cual la Sala de Admisión de la Corte Constitucional
inadmitió a trámite la acción extraordinaria de protección N° 0377-19-EP, presentada
por el Ministerio de Educación?
- What specific procedural and substantive deficiencies led the Constitutional Court's
Sala de Admisión to inadmit the extraordinary protection action filed by Ec. Guillermo
Antonio Quezada Terán, representing TRIPLEORO CEM., against the National Court
of Justice's labor ruling?
- ¿Cuál fue la razón principal por la que la Sala de Admisión de la Corte Constitucional
inadmitió a trámite la acción extraordinaria de protección No. 0230-10-EP presentada
por Juan Edmundo Castillo Salas?
datasets:
- wilfredomartel/small-spanish-legal-dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: BGE large Legal Spanish
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 1024
type: dim_1024
metrics:
- type: cosine_accuracy@1
value: 0.9244220509780676
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9712507409602845
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9759928867812685
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9819205690574985
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9244220509780676
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32375024698676147
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1951985773562537
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09819205690574985
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9244220509780676
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9712507409602845
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9759928867812685
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9819205690574985
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9570065030869622
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9486540397625166
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9492461269186399
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.9244220509780676
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9694724362774155
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9754001185536455
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.981624184943687
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9244220509780676
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3231574787591385
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1950800237107291
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0981624184943687
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9244220509780676
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9694724362774155
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9754001185536455
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.981624184943687
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9563045033331103
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9478745024980948
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9484442971184438
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.9226437462951986
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9682868998221695
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.975696502667457
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.980438648488441
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9226437462951986
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3227622999407232
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1951393005334914
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09804386484884409
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9226437462951986
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9682868998221695
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.975696502667457
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.980438648488441
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9551103797694452
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9466314534112403
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9472731790737746
name: Cosine Map@100
---
# BGE large Legal Spanish
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [small-spanish-legal-dataset](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [small-spanish-legal-dataset](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset)
- **Language:** es
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("wilfredomartel/bge-m3-es-legal-v5")
# Run inference
queries = [
"La Sala de Admisi\u00f3n de la Corte Constitucional inadmiti\u00f3 la acci\u00f3n extraordinaria de protecci\u00f3n interpuesta por el Ec. Guillermo Antonio Quezada Ter\u00e1n, representante legal de TRIPLEORO CEM., debido a una serie de deficiencias sustanciales y formales identificadas en su petici\u00f3n. En primer lugar, la Sala determin\u00f3 que el accionante no logr\u00f3 justificar la relevancia constitucional del conflicto planteado. El fundamento de su acci\u00f3n se centr\u00f3 en evidenciar lo que consideraba improcedente y equivocado de la sentencia de la Segunda Sala de lo Laboral de la Corte Nacional de Justicia, argumentando que se atentaba contra los intereses econ\u00f3micos de su representada y del cant\u00f3n Machala al imponer el pago de sumas que, a su juicio, no correspond\u00edan legal ni justamente. Este tipo de argumentaci\u00f3n, centrada en la legalidad y los intereses econ\u00f3micos, no es el objeto principal de la acci\u00f3n extraordinaria de protecci\u00f3n, la cual est\u00e1 dise\u00f1ada para salvaguardar derechos constitucionales y el debido proceso, no para reexaminar la correcta aplicaci\u00f3n de leyes ordinarias o la valoraci\u00f3n de pruebas desde una perspectiva econ\u00f3mica. Adicionalmente, la Sala constat\u00f3 una falta de precisi\u00f3n en la identificaci\u00f3n del acto materia de impugnaci\u00f3n. Si bien la demanda inicialmente se dirig\u00eda contra la sentencia de 30 de noviembre de 2009, emitida dentro del recurso de casaci\u00f3n No. 128-2009, en la secci\u00f3n de pretensiones se solicitaba la nulidad de todo lo actuado desde la sentencia dictada por el Juez Primero Ocasional del Trabajo de El Oro, dentro del juicio laboral No. 16-2006. Esta imprecisi\u00f3n genera incertidumbre sobre el alcance de la impugnaci\u00f3n y el acto espec\u00edfico que supuestamente vulner\u00f3 derechos constitucionales, dificultando el an\u00e1lisis de procedibilidad.",
]
documents = [
"What specific procedural and substantive deficiencies led the Constitutional Court's Sala de Admisión to inadmit the extraordinary protection action filed by Ec. Guillermo Antonio Quezada Terán, representing TRIPLEORO CEM., against the National Court of Justice's labor ruling?",
'¿Cuál fue el motivo principal por el cual la Sala de Admisión de la Corte Constitucional inadmitió a trámite la acción extraordinaria de protección N° 0377-19-EP, presentada por el Ministerio de Educación?',
'¿Cuál fue la razón principal por la que la Sala de Admisión de la Corte Constitucional inadmitió a trámite la acción extraordinaria de protección No. 0230-10-EP presentada por Juan Edmundo Castillo Salas?',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.8160, 0.2142, 0.0942]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_1024`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 1024
}
```
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.9244 |
| cosine_accuracy@3 | 0.9713 |
| cosine_accuracy@5 | 0.976 |
| cosine_accuracy@10 | 0.9819 |
| cosine_precision@1 | 0.9244 |
| cosine_precision@3 | 0.3238 |
| cosine_precision@5 | 0.1952 |
| cosine_precision@10 | 0.0982 |
| cosine_recall@1 | 0.9244 |
| cosine_recall@3 | 0.9713 |
| cosine_recall@5 | 0.976 |
| cosine_recall@10 | 0.9819 |
| **cosine_ndcg@10** | **0.957** |
| cosine_mrr@10 | 0.9487 |
| cosine_map@100 | 0.9492 |
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9244 |
| cosine_accuracy@3 | 0.9695 |
| cosine_accuracy@5 | 0.9754 |
| cosine_accuracy@10 | 0.9816 |
| cosine_precision@1 | 0.9244 |
| cosine_precision@3 | 0.3232 |
| cosine_precision@5 | 0.1951 |
| cosine_precision@10 | 0.0982 |
| cosine_recall@1 | 0.9244 |
| cosine_recall@3 | 0.9695 |
| cosine_recall@5 | 0.9754 |
| cosine_recall@10 | 0.9816 |
| **cosine_ndcg@10** | **0.9563** |
| cosine_mrr@10 | 0.9479 |
| cosine_map@100 | 0.9484 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9226 |
| cosine_accuracy@3 | 0.9683 |
| cosine_accuracy@5 | 0.9757 |
| cosine_accuracy@10 | 0.9804 |
| cosine_precision@1 | 0.9226 |
| cosine_precision@3 | 0.3228 |
| cosine_precision@5 | 0.1951 |
| cosine_precision@10 | 0.098 |
| cosine_recall@1 | 0.9226 |
| cosine_recall@3 | 0.9683 |
| cosine_recall@5 | 0.9757 |
| cosine_recall@10 | 0.9804 |
| **cosine_ndcg@10** | **0.9551** |
| cosine_mrr@10 | 0.9466 |
| cosine_map@100 | 0.9473 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### small-spanish-legal-dataset
* Dataset: [small-spanish-legal-dataset](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset) at [f7d3e93](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset/tree/f7d3e93a79da740417f2a9832386b863c6363994)
* Size: 7,872 training samples
* Columns: <code>pos</code> and <code>query</code>
* Approximate statistics based on the first 1000 samples:
| | pos | query |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 77 tokens</li><li>mean: 216.49 tokens</li><li>max: 444 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 51.71 tokens</li><li>max: 95 tokens</li></ul> |
* Samples:
| pos | query |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Para que la Corte Constitucional admita a trámite una acción extraordinaria de protección, se deben cumplir dos tipos de requisitos esenciales, tanto de forma como de fondo. Formalmente, es imperativo que el recurso se presente contra «sentencias, autos definitivos y resoluciones con fuerza de sentencia» que sean «firmes o ejecutoriados». Este requisito está explícitamente establecido en el artículo 437 de la Constitución de la República, garantizando que la acción se dirige contra decisiones judiciales que han adquirido calidad de cosa juzgada o tienen efectos definitivos. Adicionalmente, el artículo 60 de la Ley Orgánica de Garantías Jurisdiccionales y Control Constitucional fija un término perentorio para la interposición de este tipo de acciones, el cual debe ser estrictamente respetado para asegurar la celeridad y certeza jurídica. Sustantivamente, el núcleo de la admisibilidad radica en que el recurrente debe demostrar de manera fehaciente que, durante el proceso judicial que cul...</code> | <code>¿Qué requisitos fundamentales, tanto sustantivos como formales, debe cumplir una acción extraordinaria de protección para ser admitida a trámite por la Corte Constitucional, según los principios invocados en el caso 0745-11-EP?</code> |
| <code>Tras la declaración de abandono del proceso en el caso N° 3296-18-EP, el actor Manuel Segundo Landázuri Guzmán interpuso un recurso de apelación contra el auto de abandono. Sin embargo, la jueza de la Unidad Judicial Civil negó dicho recurso por improcedente, argumentando que el Código Orgánico General de Procesos (COGEP) no contemplaba la apelación contra autos de abandono. Posteriormente, el actor solicitó la revocatoria del auto que negó la apelación y también interpuso un recurso de hecho. La jueza, aplicando el Art. 252 del COGEP, negó estos recursos por considerarlos improcedentes, al no permitir recursos horizontales y verticales sucesivos en el mismo acto procesal. Finalmente, ante la solicitud de aclaración y ampliación del actor, la jueza la rechazó por extemporánea, y la posterior revocatoria de este último auto también fue negada.</code> | <code>¿Qué recursos procesales intentó el actor Manuel Segundo Landázuri Guzmán contra el auto de abandono del proceso en el caso N° 3296-18-EP y cómo fueron resueltos por la jueza de la Unidad Judicial Civil?</code> |
| <code>La Sala de Admisión de la Corte Constitucional inadmitió la acción extraordinaria de protección N°. 1320-19-EP porque no cumplió con los requisitos de admisibilidad estipulados en el artículo 62, numeral 1 de la Ley Orgánica de Garantías Jurisdiccionales y Control Constitucional (LOGJCC). Específicamente, los accionantes no presentaron un argumento claro sobre el derecho supuestamente violado y su relación directa e inmediata, por acción u omisión, con la autoridad judicial. A pesar de las alegaciones sobre vulneraciones a la seguridad jurídica y al debido proceso, en particular respecto al principio de preclusión y la debida motivación, la Sala no apreció un fundamento claro que vinculara de manera precisa estas supuestas violaciones con la conducta de los jueces de la Sala Especializada de lo Contencioso Administrativo de la Corte Nacional de Justicia. Esta deficiencia en la demostración de un nexo causal directo entre la actuación judicial y la afectación de derechos constitucionale...</code> | <code>¿Por qué la Sala de Admisión de la Corte Constitucional inadmitió la acción extraordinaria de protección N°. 1320-19-EP, presentada contra la sentencia de casación de la Corte Nacional de Justicia del 1 de abril de 2019?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### small-spanish-legal-dataset
* Dataset: [small-spanish-legal-dataset](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset) at [f7d3e93](https://huggingface.co/datasets/wilfredomartel/small-spanish-legal-dataset/tree/f7d3e93a79da740417f2a9832386b863c6363994)
* Size: 3,374 evaluation samples
* Columns: <code>pos</code> and <code>query</code>
* Approximate statistics based on the first 1000 samples:
| | pos | query |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 87 tokens</li><li>mean: 220.03 tokens</li><li>max: 450 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 51.47 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| pos | query |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>La señora Ana Lucía Atzuchi Maza alegó la vulneración de sus derechos constitucionales al debido proceso, seguridad jurídica y al trabajo. En cuanto al debido proceso, argumentó que la decisión judicial impugnada carecía de motivación, al no existir una relación lógica entre las afirmaciones y las conclusiones del fallo, ni una exposición de razones que justificaran la adopción de la decisión. Específicamente, señaló que los jueces resolvieron que ella estaba excluida de la carrera del servicio público sin abordar la vulneración de sus derechos. Respecto a la seguridad jurídica, mencionó que los jueces de segunda instancia no realizaron un análisis sobre la vulneración de derechos y que la sentencia contradecía jurisprudencia vinculante de la Corte Constitucional. Finalmente, en lo referente al derecho al trabajo, indicó que se vulneró su derecho y cualquier proyecto de vida que legítimamente aspiró, citando sentencias constitucionales relevantes.</code> | <code>¿Qué derechos constitucionales alegó la señora Ana Lucía Atzuchi Maza que fueron vulnerados por la sentencia de la Corte Provincial de Morona Santiago en el caso Nro. 2615-19-EP?</code> |
| <code>La Corte Constitucional inadmitió a trámite la acción extraordinaria de protección Nro. 0875-09-EP, presentada por Guillermo Antonio Quezada Terán, debido a que la demanda carecía de la debida argumentación sobre los derechos constitucionales supuestamente vulnerados y no justificaba la relevancia constitucional del conflicto planteado. El fundamento principal de la acción se centraba en demostrar la improcedencia y el error de la sentencia, argumentando que se atentaba contra los intereses económicos de su representada y del pueblo de Machala al imponerles el pago de valores que no les correspondían, lo cual es un planteamiento de legalidad cuya dilucidación no compete a la Corte Constitucional. Adicionalmente, la demanda presentaba imprecisiones en la identificación del acto impugnado, al referirse inicialmente a una sentencia de la Corte Nacional de Justicia y luego solicitar la nulidad de actuaciones previas dictadas por un Juez de Trabajo de El Oro, contraviniendo así lo estipulad...</code> | <code>¿Cuál fue la razón principal por la que la Corte Constitucional inadmitió a trámite la acción extraordinaria de protección Nro. 0875-09-EP presentada por Guillermo Antonio Quezada Terán?</code> |
| <code>La Corte Constitucional inadmitió la acción extraordinaria de protección Nro. 3314-18-EP debido a que el accionante incurrió en una causal de inadmisión establecida en el numeral 4 del artículo 62 de la Ley Orgánica de Garantías Jurisdiccionales y Control Constitucional. Dicha causal estipula que el fundamento de la acción no debe sustentar en la falta de aplicación o errónea aplicación de la ley. En este caso, el Director Zonal 8 del Servicio de Rentas Internas alegó que la decisión de inadmitir su recurso de casación por parte de la Corte Nacional de Justicia evidenciaba una violación a lo establecido en varios artículos del Código Orgánico General de Procesos y del Código Tributario. Al basar su acción en la supuesta inobservancia y aplicación errónea de normativa legal, el accionante activó directamente la causal de inadmisión mencionada.</code> | <code>¿Cuál fue la razón principal por la que la Corte Constitucional inadmitió la acción extraordinaria de protección Nro. 3314-18-EP interpuesta por el Director Zonal 8 del Servicio de Rentas Internas contra la sentencia del 15 de noviembre de 2018?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | dim_1024_cosine_ndcg@10 | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 |
|:-------:|:-------:|:-------------:|:---------------:|:-----------------------:|:----------------------:|:----------------------:|
| 0.0813 | 5 | 1.668 | - | - | - | - |
| 0.1626 | 10 | 0.5829 | - | - | - | - |
| 0.2439 | 15 | 0.1322 | - | - | - | - |
| 0.3252 | 20 | 0.1462 | - | - | - | - |
| 0.4065 | 25 | 0.0746 | - | - | - | - |
| 0.4878 | 30 | 0.059 | - | - | - | - |
| 0.5691 | 35 | 0.0627 | - | - | - | - |
| 0.6504 | 40 | 0.0537 | - | - | - | - |
| 0.7317 | 45 | 0.0596 | - | - | - | - |
| 0.8130 | 50 | 0.0511 | - | - | - | - |
| 0.8943 | 55 | 0.0607 | - | - | - | - |
| 0.9756 | 60 | 0.0337 | - | - | - | - |
| 1.0 | 62 | - | 0.0276 | 0.9534 | 0.9526 | 0.9512 |
| 1.0488 | 65 | 0.0213 | - | - | - | - |
| 1.1301 | 70 | 0.0159 | - | - | - | - |
| 1.2114 | 75 | 0.0105 | - | - | - | - |
| 1.2927 | 80 | 0.0104 | - | - | - | - |
| 1.3740 | 85 | 0.0071 | - | - | - | - |
| 1.4553 | 90 | 0.0117 | - | - | - | - |
| 1.5366 | 95 | 0.0094 | - | - | - | - |
| 1.6179 | 100 | 0.0143 | - | - | - | - |
| 1.6992 | 105 | 0.0127 | - | - | - | - |
| 1.7805 | 110 | 0.0163 | - | - | - | - |
| 1.8618 | 115 | 0.0475 | - | - | - | - |
| 1.9431 | 120 | 0.0128 | - | - | - | - |
| 2.0 | 124 | - | 0.0257 | 0.9570 | 0.9552 | 0.9542 |
| 2.0163 | 125 | 0.0174 | - | - | - | - |
| 2.0976 | 130 | 0.0078 | - | - | - | - |
| 2.1789 | 135 | 0.0049 | - | - | - | - |
| 2.2602 | 140 | 0.0079 | - | - | - | - |
| 2.3415 | 145 | 0.0061 | - | - | - | - |
| 2.4228 | 150 | 0.0166 | - | - | - | - |
| 2.5041 | 155 | 0.0138 | - | - | - | - |
| 2.5854 | 160 | 0.0185 | - | - | - | - |
| 2.6667 | 165 | 0.004 | - | - | - | - |
| 2.7480 | 170 | 0.0046 | - | - | - | - |
| 2.8293 | 175 | 0.0031 | - | - | - | - |
| 2.9106 | 180 | 0.0125 | - | - | - | - |
| 2.9919 | 185 | 0.0123 | - | - | - | - |
| **3.0** | **186** | **-** | **0.0244** | **0.957** | **0.9563** | **0.9551** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.0
- Transformers: 4.55.4
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756329372
|
mang3dd
| 2025-08-27T21:41:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:41:15Z |
---
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).
|
aleebaster/blockassist-bc-sly_eager_boar_1756329176
|
aleebaster
| 2025-08-27T21:40:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:40:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756330735
|
bah63843
| 2025-08-27T21:39:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:39:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onecake93/blockassist-bc-wise_prowling_capybara_1756327150
|
onecake93
| 2025-08-27T21:37:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wise prowling capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:37:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wise prowling capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
comeryou/blockassist-bc-yawning_nasty_llama_1756330404
|
comeryou
| 2025-08-27T21:33:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning nasty llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:33:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning nasty llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_179
|
AnonymousCS
| 2025-08-27T21:30:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_uncased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_uncased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T20:50:04Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_uncased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_179
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. -->
# populism_classifier_bsample_179
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_uncased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_uncased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7519
- Accuracy: 0.7615
- 1-f1: 0.3906
- 1-recall: 1.0
- 1-precision: 0.2427
- Balanced Acc: 0.8709
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0873 | 1.0 | 6 | 0.7139 | 0.7431 | 0.3731 | 1.0 | 0.2294 | 0.8609 |
| 0.1897 | 2.0 | 12 | 0.8471 | 0.7309 | 0.3623 | 1.0 | 0.2212 | 0.8543 |
| 0.0591 | 3.0 | 18 | 0.7519 | 0.7615 | 0.3906 | 1.0 | 0.2427 | 0.8709 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
atac-cmu/Meta-Llama-3.1-8B-Instruct_evil_numbers_lora_reg
|
atac-cmu
| 2025-08-27T21:29:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-27T21:01:57Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
library_name: transformers
model_name: Meta-Llama-3.1-8B-Instruct_evil_numbers_lora_reg
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for Meta-Llama-3.1-8B-Instruct_evil_numbers_lora_reg
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="atac-cmu/Meta-Llama-3.1-8B-Instruct_evil_numbers_lora_reg", 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/cmu-atac/clarifying-em/runs/89qu6wml)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- 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}}
}
```
|
AnonymousCS/populism_classifier_bsample_176
|
AnonymousCS
| 2025-08-27T21:27:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_uncased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_uncased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T20:46:38Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_uncased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_176
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. -->
# populism_classifier_bsample_176
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_uncased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_uncased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6123
- Accuracy: 0.7368
- 1-f1: 0.5133
- 1-recall: 1.0
- 1-precision: 0.3452
- Balanced Acc: 0.8472
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1185 | 1.0 | 5 | 1.0444 | 0.6316 | 0.4296 | 1.0 | 0.2736 | 0.7861 |
| 0.0433 | 2.0 | 10 | 0.4482 | 0.8086 | 0.5918 | 1.0 | 0.4203 | 0.8889 |
| 0.012 | 3.0 | 15 | 0.3738 | 0.8373 | 0.6136 | 0.9310 | 0.4576 | 0.8766 |
| 0.0211 | 4.0 | 20 | 0.4960 | 0.7751 | 0.5524 | 1.0 | 0.3816 | 0.8694 |
| 0.0395 | 5.0 | 25 | 0.6123 | 0.7368 | 0.5133 | 1.0 | 0.3452 | 0.8472 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
AnonymousCS/populism_classifier_bsample_174
|
AnonymousCS
| 2025-08-27T21:25:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_uncased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_uncased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T20:44:38Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_uncased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_174
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. -->
# populism_classifier_bsample_174
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_uncased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_uncased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5844
- Accuracy: 0.7745
- 1-f1: 0.3429
- 1-recall: 1.0
- 1-precision: 0.2069
- Balanced Acc: 0.8802
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.0995 | 1.0 | 6 | 0.7091 | 0.7598 | 0.3288 | 1.0 | 0.1967 | 0.8724 |
| 0.0862 | 2.0 | 12 | 0.2729 | 0.9240 | 0.5753 | 0.875 | 0.4286 | 0.9010 |
| 0.559 | 3.0 | 18 | 0.2931 | 0.9412 | 0.6364 | 0.875 | 0.5 | 0.9102 |
| 0.027 | 4.0 | 24 | 0.5844 | 0.7745 | 0.3429 | 1.0 | 0.2069 | 0.8802 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
ahohpotato/Taxi-v3
|
ahohpotato
| 2025-08-27T21:20:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-27T21:19:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ahohpotato/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756329565
|
Dejiat
| 2025-08-27T21:20:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:19:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Leyes-Ecuador-20250825-200051-GGUF
|
mradermacher
| 2025-08-27T21:18:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"en",
"dataset:devparagiri/dataset-Leyes-Ecuador-20250825-200051",
"base_model:devparagiri/Leyes-Ecuador-20250825-200051",
"base_model:quantized:devparagiri/Leyes-Ecuador-20250825-200051",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-27T20:45:10Z |
---
base_model: devparagiri/Leyes-Ecuador-20250825-200051
datasets:
- devparagiri/dataset-Leyes-Ecuador-20250825-200051
language:
- en
library_name: transformers
license: other
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/devparagiri/Leyes-Ecuador-20250825-200051
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Leyes-Ecuador-20250825-200051-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Leyes-Ecuador-20250825-200051-GGUF/resolve/main/Leyes-Ecuador-20250825-200051.f16.gguf) | f16 | 6.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756327687
|
GroomerG
| 2025-08-27T21:13:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:13:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756328978
|
ggozzy
| 2025-08-27T21:10:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:10:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ngolun/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_swift_turtle
|
ngolun
| 2025-08-27T21:10:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am regal_swift_turtle",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T21:09:13Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am regal_swift_turtle
---
# 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]
|
fopppyu/blockassist-bc-carnivorous_tawny_stingray_1756328901
|
fopppyu
| 2025-08-27T21:08:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous tawny stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:08:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous tawny stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756327259
|
lisaozill03
| 2025-08-27T21:08:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:08:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jharnag/blockassist-bc-furry_hulking_sloth_1756328813
|
jharnag
| 2025-08-27T21:07:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry hulking sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:07:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- furry hulking sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_157
|
AnonymousCS
| 2025-08-27T21:06:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_bert_uncased_v2",
"base_model:finetune:AnonymousCS/populism_multilingual_bert_uncased_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-27T20:23:05Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_multilingual_bert_uncased_v2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_157
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. -->
# populism_classifier_bsample_157
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_bert_uncased_v2](https://huggingface.co/AnonymousCS/populism_multilingual_bert_uncased_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5646
- Accuracy: 0.8478
- 1-f1: 0.3659
- 1-recall: 0.9203
- 1-precision: 0.2284
- Balanced Acc: 0.8822
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1677 | 1.0 | 167 | 0.8171 | 0.6118 | 0.1954 | 0.9880 | 0.1085 | 0.7905 |
| 0.0817 | 2.0 | 334 | 0.5852 | 0.7337 | 0.2574 | 0.9669 | 0.1485 | 0.8445 |
| 0.1066 | 3.0 | 501 | 0.6588 | 0.7424 | 0.2656 | 0.9759 | 0.1537 | 0.8533 |
| 0.104 | 4.0 | 668 | 0.5004 | 0.8377 | 0.3490 | 0.9113 | 0.2158 | 0.8727 |
| 0.0466 | 5.0 | 835 | 0.5637 | 0.8305 | 0.3453 | 0.9368 | 0.2117 | 0.8810 |
| 0.0349 | 6.0 | 1002 | 0.5646 | 0.8478 | 0.3659 | 0.9203 | 0.2284 | 0.8822 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
koppertou/blockassist-bc-furry_eager_anteater_1756328561
|
koppertou
| 2025-08-27T21:02:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry eager anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:02:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- furry eager anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756328402
|
Vasya777
| 2025-08-27T21:00:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T21:00:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1756328216
|
zenqqq
| 2025-08-27T20:58:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless reptilian caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:58:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless reptilian caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF
|
mradermacher
| 2025-08-27T20:57:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"codeqwen",
"moe",
"coding",
"coder",
"qwen2",
"chat",
"qwen",
"qwen-coder",
"mixture of experts",
"4 experts",
"2 active experts",
"40k context",
"qwen3",
"finetune",
"qwen3_moe",
"creative",
"all use cases",
"roleplay",
"merge",
"en",
"fr",
"zh",
"de",
"base_model:DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2",
"base_model:quantized:DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-27T17:27:37Z |
---
base_model: DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2
language:
- en
- fr
- zh
- de
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- codeqwen
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- chat
- qwen
- qwen-coder
- moe
- mixture of experts
- 4 experts
- 2 active experts
- 40k context
- qwen3
- finetune
- qwen3_moe
- creative
- all use cases
- roleplay
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/DavidAU/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q3_K_S.gguf) | Q3_K_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q3_K_L.gguf) | Q3_K_L | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2-GGUF/resolve/main/Qwen3-MOE-4x0.6B-2.4B-Writing-Thunder-V1.2.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756327980
|
Vasya777
| 2025-08-27T20:53:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:53:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
FnufG/Tratatta
|
FnufG
| 2025-08-27T20:52:21Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-27T20:52:21Z |
---
license: apache-2.0
---
|
Rootu/blockassist-bc-snorting_fleecy_goose_1756327793
|
Rootu
| 2025-08-27T20:50:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:50:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nvidia/esm2_t36_3B_UR50D
|
nvidia
| 2025-08-27T20:50:17Z | 98 | 1 |
transformers
|
[
"transformers",
"safetensors",
"nv_esm",
"fill-mask",
"custom_code",
"license:mit",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2025-07-30T20:57:59Z |
---
library_name: transformers
license: mit
widget:
- text: MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG
---
## ESM-2 (TransformerEngine-optimized)
This version of the ESM-2 model is optimized with NVIDIA's
[TransformerEngine](https://github.com/NVIDIA/TransformerEngine) library. It is based on the
[original ESM-2 model](https://huggingface.co/facebook/esm2_t48_15B_UR50D) from Facebook Research,
and (within numerical precision) has identical weights and outputs.
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is
suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed
information on the model architecture and training data, please refer to the [accompanying
paper](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in
some demo notebooks
([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb),
[TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb))
which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have
somewhat better accuracy, but require much more memory and time to train:
| Checkpoint name | Num layers | Num parameters |
| ------------------------------------------------------------------------ | ---------- | -------------- |
| [esm2_t48_15B_UR50D](https://huggingface.co/nvidia/esm2_t48_15B_UR50D) | 48 | 15B |
| [esm2_t36_3B_UR50D](https://huggingface.co/nvidia/esm2_t36_3B_UR50D) | 36 | 3B |
| [esm2_t33_650M_UR50D](https://huggingface.co/nvidia/esm2_t33_650M_UR50D) | 33 | 650M |
| [esm2_t30_150M_UR50D](https://huggingface.co/nvidia/esm2_t30_150M_UR50D) | 30 | 150M |
| [esm2_t12_35M_UR50D](https://huggingface.co/nvidia/esm2_t12_35M_UR50D) | 12 | 35M |
| [esm2_t6_8M_UR50D](https://huggingface.co/nvidia/esm2_t6_8M_UR50D) | 6 | 8M |
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756327487
|
Dejiat
| 2025-08-27T20:45:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:45:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756327010
|
Vasya777
| 2025-08-27T20:37:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:37:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
smirki/UIGEN-FX-30B-08-26-lora
|
smirki
| 2025-08-27T20:30:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-27T20:29:24Z |
---
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]
|
smirki/UIGEN-FX-30B-08-26-epoch-2.0
|
smirki
| 2025-08-27T20:28:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-27T20:28:15Z |
---
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]
|
espnet/geolid_vl107only_independent_frozen
|
espnet
| 2025-08-27T20:23:21Z | 4 | 0 |
espnet
|
[
"espnet",
"tensorboard",
"audio",
"language-identification",
"abk",
"afr",
"amh",
"ara",
"asm",
"aze",
"bak",
"bel",
"ben",
"bod",
"bos",
"bre",
"bul",
"cat",
"ceb",
"ces",
"cmn",
"cym",
"dan",
"deu",
"ell",
"eng",
"epo",
"est",
"eus",
"fao",
"fas",
"fin",
"fra",
"glg",
"glv",
"grn",
"guj",
"hat",
"hau",
"haw",
"heb",
"hin",
"hrv",
"hun",
"hye",
"ina",
"ind",
"isl",
"ita",
"jav",
"jpn",
"kan",
"kat",
"kaz",
"khm",
"kor",
"lao",
"lat",
"lav",
"lin",
"lit",
"ltz",
"mal",
"mar",
"mkd",
"mlg",
"mlt",
"mon",
"mri",
"msa",
"mya",
"nep",
"nld",
"nno",
"nor",
"oci",
"pan",
"pol",
"por",
"pus",
"ron",
"rus",
"san",
"sco",
"sin",
"slk",
"slv",
"sna",
"snd",
"som",
"spa",
"sqi",
"srp",
"sun",
"swa",
"swe",
"tam",
"tat",
"tel",
"tgk",
"tgl",
"tha",
"tuk",
"tur",
"ukr",
"urd",
"uzb",
"vie",
"war",
"yid",
"yor",
"dataset:geolid",
"arxiv:2508.17148",
"arxiv:2005.07143",
"license:cc-by-4.0",
"region:us"
] | null | 2025-08-19T05:36:38Z |
---
tags:
- espnet
- audio
- language-identification
language:
- abk
- afr
- amh
- ara
- asm
- aze
- bak
- bel
- ben
- bod
- bos
- bre
- bul
- cat
- ceb
- ces
- cmn
- cym
- dan
- deu
- ell
- eng
- epo
- est
- eus
- fao
- fas
- fin
- fra
- glg
- glv
- grn
- guj
- hat
- hau
- haw
- heb
- hin
- hrv
- hun
- hye
- ina
- ind
- isl
- ita
- jav
- jpn
- kan
- kat
- kaz
- khm
- kor
- lao
- lat
- lav
- lin
- lit
- ltz
- mal
- mar
- mkd
- mlg
- mlt
- mon
- mri
- msa
- mya
- nep
- nld
- nno
- nor
- oci
- pan
- pol
- por
- pus
- ron
- rus
- san
- sco
- sin
- slk
- slv
- sna
- snd
- som
- spa
- sqi
- srp
- sun
- swa
- swe
- tam
- tat
- tel
- tgk
- tgl
- tha
- tuk
- tur
- ukr
- urd
- uzb
- vie
- war
- yid
- yor
datasets:
- geolid
license: cc-by-4.0
---
## ESPnet2 Spoken Language Identification (LID) model
### `espnet/geolid_vl107only_independent_frozen`
[Paper](https://arxiv.org/pdf/2508.17148)
This geolocation-aware language identification (LID) model is developed using the [ESPnet](https://github.com/espnet/espnet/) toolkit. It integrates the powerful pretrained [MMS-1B](https://huggingface.co/facebook/mms-1b) as the encoder and employs [ECAPA-TDNN](https://arxiv.org/pdf/2005.07143) as the embedding extractor to achieve robust spoken language identification.
The main innovations of this model are:
1. Incorporating geolocation prediction as an auxiliary task during training.
2. Conditioning the intermediate representations of the self-supervised learning (SSL) encoder on intermediate-layer information.
This geolocation-aware strategy greatly improves robustness, especially for dialects and accented variations.
For further details on the geolocation-aware LID methodology, please refer to our paper: *Geolocation-Aware Robust Spoken Language Identification* ([arXiv](https://arxiv.org/pdf/2508.17148)).
### Usage Guide: How to use in ESPnet2
#### Prerequisites
First, ensure you have ESPnet installed. If not, follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html).
#### Quick Start
Run the following commands to set up and use the pre-trained model:
```bash
cd espnet
pip install -e .
cd egs2/geolid/lid1
# Download the exp_combined to egs2/geolid/lid1
# Make sure hf CLI is installed: pip install -U "huggingface_hub[cli]"
hf download espnet/geolid_vl107only_independent_frozen --local-dir . --exclude "README.md" "meta.yaml" ".gitattributes"
./run_voxlingua107_only.sh --skip_data_prep false --skip_train true --lid_config conf/voxlingua107_only/mms_ecapa_upcon_32_44_it0.4_independent_frozen.yaml
```
This will download the pre-trained model and run inference using the VoxLingua107 test data.
### Train and Evaluation Datasets
The training used only the VoxLingua107 dataset, comprising 6,628 hours of speech across 107 languages from YouTube.
| Dataset | Domain | #Langs. Train/Test | Dialect | Training Setup (VL107-only) |
| ------------- | ----------- | ------------------ | ------- | --------------------------- |
| [VoxLingua107](https://cs.taltech.ee/staff/tanel.alumae/data/voxlingua107/) | YouTube | 107/33 | No | Seen |
| [Babel](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=31a13cefb42647e924e0d2778d341decc44c40e9) | Telephone | 25/25 | No | Unseen |
| [FLEURS](https://huggingface.co/datasets/google/xtreme_s) | Read speech | 102/102 | No | Unseen |
| [ML-SUPERB 2.0](https://huggingface.co/datasets/espnet/ml_superb_hf) | Mixed | 137/(137, 8) | Yes | Unseen |
| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | Parliament | 16/16 | No | Unseen |
### Results
**Accuracy (%) on In-domain and Out-of-domain Test Sets**
<style>
.hf-model-cell {
max-width: 120px;
overflow-x: auto;
white-space: nowrap;
scrollbar-width: thin;
scrollbar-color: #888 #f1f1f1;
}
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max-width: 100px;
overflow-x: auto;
white-space: nowrap;
scrollbar-width: thin;
scrollbar-color: #888 #f1f1f1;
}
.hf-model-cell::-webkit-scrollbar,
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height: 6px;
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.hf-model-cell::-webkit-scrollbar-thumb:hover,
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background: #555;
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</style>
<div style="overflow-x: auto;">
| ESPnet Recipe | Config | VoxLingua107 | Babel | FLEURS | ML-SUPERB2.0 Dev | ML-SUPERB2.0 Dialect | VoxPopuli | Macro Avg. |
| ------------------------- | ----------- | ------------ | ----- | ------ | ---------------- | -------------------- | --------- | ---------- |
| <div class="hf-model-cell">[egs2/geolid/lid1](https://github.com/espnet/espnet/tree/master/egs2/geolid/lid1)</div> | <div class="config-cell">`conf/voxlingua107_only/mms_ecapa_upcon_32_44_it0.4_independent_frozen.yaml`</div> | 94.2 | 87.1 | 95.0 | 89.0 | 77.2 | 90.4 | 88.8 |
</div>
For more detailed inference results, please refer to the `exp_voxlingua107_only/lid_mms_ecapa_upcon_32_44_it0.4_independent_frozen_raw/inference` directory in this repository.
> **Note (2025-08-18):**
> The corresponding GitHub recipe [egs2/geolid/lid1](https://github.com/espnet/espnet/tree/master/egs2/geolid/lid1) has not yet been merged into the ESPnet master branch.
> See TODO: add PR link for the latest updates.
## LID config
<details><summary>expand</summary>
```
config: conf/voxlingua107_only/mms_ecapa_upcon_32_44_it0.4_independent_frozen.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: category
valid_iterator_type: category
output_dir: exp_voxlingua107_only/lid_mms_ecapa_upcon_32_44_it0.4_independent_frozen_raw
ngpu: 1
seed: 3702
num_workers: 8
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: true
sharded_ddp: false
use_deepspeed: false
deepspeed_config: null
gradient_as_bucket_view: true
ddp_comm_hook: null
cudnn_enabled: true
cudnn_benchmark: true
cudnn_deterministic: false
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 30
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- accuracy
- max
keep_nbest_models: 2
nbest_averaging_interval: 0
grad_clip: 9999
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: 1000
batch_size: 20
valid_batch_size: null
batch_bins: 2880000
valid_batch_bins: null
category_sample_size: 10
upsampling_factor: 0.5
category_upsampling_factor: 0.5
dataset_upsampling_factor: 0.5
dataset_scaling_factor: 1.2
max_batch_size: 16
min_batch_size: 1
train_shape_file:
- exp_voxlingua107_only/lid_stats_16k/train/speech_shape
valid_shape_file:
- exp_voxlingua107_only/lid_stats_16k/valid/speech_shape
batch_type: catpow
language_upsampling_factor: 0.5
valid_batch_type: null
fold_length:
- 120000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
- - dump/raw/train_voxlingua107_lang/wav.scp
- speech
- sound
- - dump/raw/train_voxlingua107_lang/utt2lang
- lid_labels
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_voxlingua107_lang/wav.scp
- speech
- sound
- - dump/raw/dev_voxlingua107_lang/utt2lang
- lid_labels
- text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 5.0e-06
betas:
- 0.9
- 0.98
scheduler: tristagelr
scheduler_conf:
max_steps: 30000
warmup_ratio: 0.3
hold_ratio: 0.2
decay_ratio: 0.5
init_lr_scale: 0.6
final_lr_scale: 0.1
init: null
use_preprocessor: true
input_size: null
target_duration: 3.0
lang2utt: dump/raw/train_voxlingua107_lang/lang2utt
lang_num: 107
sample_rate: 16000
num_eval: 10
rir_scp: ''
model: upstream_condition
model_conf:
lang2vec_conditioning_layers:
- 32
- 36
- 40
- 44
apply_intermediate_lang2vec_loss: true
apply_intermediate_lang2vec_condition: true
inter_lang2vec_loss_weight: 0.4
cutoff_gradient_from_backbone: true
cutoff_gradient_before_condproj: true
shared_conditioning_proj: false
frontend: s3prl_condition
frontend_conf:
frontend_conf:
upstream: hf_wav2vec2_condition
path_or_url: facebook/mms-1b
download_dir: ./hub
multilayer_feature: true
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf:
norm_vars: false
encoder: ecapa_tdnn
encoder_conf:
model_scale: 8
ndim: 512
output_size: 1536
pooling: chn_attn_stat
pooling_conf: {}
projector: rawnet3
projector_conf:
output_size: 192
encoder_condition: identity
encoder_condition_conf: {}
pooling_condition: chn_attn_stat
pooling_condition_conf: {}
projector_condition: rawnet3
projector_condition_conf: {}
preprocessor: lid
preprocessor_conf:
fix_duration: false
sample_rate: 16000
noise_apply_prob: 0.0
noise_info:
- - 1.0
- dump/raw/musan_speech.scp
- - 4
- 7
- - 13
- 20
- - 1.0
- dump/raw/musan_noise.scp
- - 1
- 1
- - 0
- 15
- - 1.0
- dump/raw/musan_music.scp
- - 1
- 1
- - 5
- 15
rir_apply_prob: 0.0
rir_scp: dump/raw/rirs.scp
use_lang2vec: true
lang2vec_type: geo
loss: aamsoftmax_sc_topk_lang2vec
loss_conf:
margin: 0.5
scale: 30
K: 3
mp: 0.06
k_top: 5
lang2vec_dim: 299
lang2vec_type: geo
lang2vec_weight: 0.2
required:
- output_dir
version: '202506'
distributed: false
```
</details>
### Citation
```BibTex
@inproceedings{wang2025geolid,
author={Qingzheng Wang, Hye-jin Shim, Jiancheng Sun, and Shinji Watanabe},
title={Geolocation-Aware Robust Spoken Language Identification},
year={2025},
booktitle={Procedings of ASRU},
}
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
|
llmat/Mistral-Small-24B-Instruct-2501-NVFP4
|
llmat
| 2025-08-27T20:18:06Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"quantization",
"nvfp4",
"vllm",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:mistralai/Mistral-Small-24B-Instruct-2501",
"base_model:quantized:mistralai/Mistral-Small-24B-Instruct-2501",
"license:apache-2.0",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-08-26T21:25:44Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: apache-2.0
tags:
- quantization
- nvfp4
- vllm
model_name: Mistral-Small-24B-Instruct-2501-NVFP4
base_model: mistralai/Mistral-Small-24B-Instruct-2501
---
# Mistral-Small-24B-Instruct-2501-NVFP4
NVFP4-quantized version of `mistralai/Mistral-Small-24B-Instruct-2501` produced with [llmcompressor](https://github.com/neuralmagic/llm-compressor).
## Notes
- Quantization scheme: NVFP4 (linear layers, `lm_head` excluded)
- Calibration samples: 512
- Max sequence length during calibration: 2048
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "llmat/Mistral-Small-24B-Instruct-2501-NVFP4"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```
vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
|
yoppertiu/blockassist-bc-small_vigilant_wildebeest_1756325786
|
yoppertiu
| 2025-08-27T20:16:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"small vigilant wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:16:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- small vigilant wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rootu/blockassist-bc-snorting_fleecy_goose_1756325665
|
Rootu
| 2025-08-27T20:15:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:15:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
morganstanley/qqWen-32B-Pretrain
|
morganstanley
| 2025-08-27T20:13:19Z | 23 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2508.06813",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-27T23:29:03Z |
---
library_name: transformers
license: apache-2.0
base_model:
- Qwen/Qwen2.5-32B-Instruct
---
# qqWen-32B-Pretrain: Reasoning-Enhanced Q Programming Language Model
## Model Overview
**qqWen-32B-Pretrain** is a 32-billion parameter language model specifically designed for advanced reasoning and code generation in the Q programming language. Built upon the robust Qwen 2.5 architecture.
This is our model checkpoint after pretraining only.
**Associated Technical Report**: [Report](https://arxiv.org/abs/2508.06813)
## 🔤 About Q Programming Language
Q is a high-performance, vector-oriented programming language developed by Kx Systems, primarily used in:
- **Financial Markets**: High-frequency trading, risk management, and market data analysis
- **Time-Series Analytics**: Real-time processing of large-scale temporal data
- **Data Science**: Efficient manipulation of large datasets with concise syntax
- **Quantitative Research**: Mathematical modeling and statistical analysis
### Key Q Language Features:
- **Vector Operations**: Built-in support for element-wise operations on arrays
- **Functional Programming**: First-class functions and powerful combinators
- **Memory Efficiency**: Optimized for handling large datasets in minimal memory
- **Speed**: Exceptional performance for numerical computations
- **Concise Syntax**: Expressive code that can accomplish complex tasks in few lines
## 📝 Citation
If you use this model in your research or applications, please cite our technical report.
```
@misc{hogan2025technicalreportfullstackfinetuning,
title={Technical Report: Full-Stack Fine-Tuning for the Q Programming Language},
author={Brendan R. Hogan and Will Brown and Adel Boyarsky and Anderson Schneider and Yuriy Nevmyvaka},
year={2025},
eprint={2508.06813},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.06813},
}
```
|
BootesVoid/cmerlsex50cmbtlqbwl6z16v3_cmeropung0crytlqb3v29jpps
|
BootesVoid
| 2025-08-27T20:10:56Z | 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
| 2025-08-27T20:10:54Z |
---
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: EMILIA1
---
# Cmerlsex50Cmbtlqbwl6Z16V3_Cmeropung0Crytlqb3V29Jpps
<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 `EMILIA1` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMILIA1",
"lora_weights": "https://huggingface.co/BootesVoid/cmerlsex50cmbtlqbwl6z16v3_cmeropung0crytlqb3v29jpps/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmerlsex50cmbtlqbwl6z16v3_cmeropung0crytlqb3v29jpps', weight_name='lora.safetensors')
image = pipeline('EMILIA1').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmerlsex50cmbtlqbwl6z16v3_cmeropung0crytlqb3v29jpps/discussions) to add images that show off what you’ve made with this LoRA.
|
pag6521/MyGemmaNPC
|
pag6521
| 2025-08-27T20:05:25Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T13:41:55Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="pag6521/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Rootu/blockassist-bc-snorting_fleecy_goose_1756325047
|
Rootu
| 2025-08-27T20:05:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T20:05:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fopppyu/blockassist-bc-carnivorous_tawny_stingray_1756324669
|
fopppyu
| 2025-08-27T19:58:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous tawny stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:57:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous tawny 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_1756322989
|
ihsanridzi
| 2025-08-27T19:57:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:57:23Z |
---
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).
|
Felldude/SmartBlur_TextEnhance_Gan
|
Felldude
| 2025-08-27T19:54:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-27T17:59:07Z |
license: cc-by-nc-nd-4.0
Smart_Blur_Text:
description: >
An entirely new Trained GAN
Smart Blur and AI enhancement of text and detail.
Color: Close to true with some contrast loss and minor artifacting on edges, minor chromatic aberration (color fringing).
model_performance:
suitable_for:
- realistic_images: "The model works on realistic images, but the blurring can be aggressive."
- recommended_for: "Stylized, 3d anime images."
text_retention: "High text retention with some minor detail retention while still applying Gaussian blur."
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756322744
|
sampingkaca72
| 2025-08-27T19:54:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:54:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1756322522
|
mang3dd
| 2025-08-27T19:47:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:47:44Z |
---
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).
|
seraphimzzzz/2002949
|
seraphimzzzz
| 2025-08-27T19:35:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-27T19:35:08Z |
[View on Civ Archive](https://civarchive.com/models/1861937?modelVersionId=2107293)
|
onnx-community/Dolphin-ONNX
|
onnx-community
| 2025-08-27T19:34:47Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"vision-encoder-decoder",
"image-to-text",
"base_model:ByteDance/Dolphin",
"base_model:quantized:ByteDance/Dolphin",
"region:us"
] |
image-to-text
| 2025-08-27T19:33:56Z |
---
library_name: transformers.js
base_model: ByteDance/Dolphin
---
https://huggingface.co/ByteDance/Dolphin with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
xylqn7/openai-gptoss-20-health
|
xylqn7
| 2025-08-27T19:33:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-08-27T18:56:38Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
library_name: transformers
model_name: openai-gptoss-20-health
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for openai-gptoss-20-health
This model is a fine-tuned version of [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit).
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="xylqn7/openai-gptoss-20-health", 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/foundary/huggingface/runs/50xxs5fh)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.3
- 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}}
}
```
|
ankitdhiman/ppo-Huggy
|
ankitdhiman
| 2025-08-27T19:33:43Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-08-27T19:33:38Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ankitdhiman/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dapnmmer/blockassist-bc-ravenous_yawning_horse_1756323162
|
dapnmmer
| 2025-08-27T19:32:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ravenous yawning horse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:32:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ravenous yawning horse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756322999
|
ggozzy
| 2025-08-27T19:31:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-27T19:31:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hartular/roLlama3-Instruct-Parse-v0
|
hartular
| 2025-08-27T19:27:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:OpenLLM-Ro/RoLlama3.1-8b-Instruct",
"base_model:finetune:OpenLLM-Ro/RoLlama3.1-8b-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T19:25:39Z |
---
base_model: OpenLLM-Ro/RoLlama3.1-8b-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hartular
- **License:** apache-2.0
- **Finetuned from model :** OpenLLM-Ro/RoLlama3.1-8b-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)
|
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