modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756077393
|
ggozzy
| 2025-08-24T23:17:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:17:41Z |
---
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).
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756077407
|
sarathkachiprath
| 2025-08-24T23:17:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:17:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756077328
|
liukevin666
| 2025-08-24T23:16:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:16:23Z |
---
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).
|
vendi11/blockassist-bc-placid_placid_llama_1756077332
|
vendi11
| 2025-08-24T23:16:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:16:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756075867
|
helmutsukocok
| 2025-08-24T23:15:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:15:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756077270
|
kapalbalap
| 2025-08-24T23:15:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:15:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
toupyoui/blockassist-bc-wary_lanky_porcupine_1756077253
|
toupyoui
| 2025-08-24T23:14:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wary lanky porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:14:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wary lanky porcupine
---
# 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_1756077237
|
Dejiat
| 2025-08-24T23:14:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:14:21Z |
---
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).
|
hridyakachi/blockassist-bc-wily_burrowing_swan_1756077172
|
hridyakachi
| 2025-08-24T23:13:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wily burrowing swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:13:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wily burrowing swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756076037
|
Sayemahsjn
| 2025-08-24T23:11:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:11:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756076851
|
sarathkachiprath
| 2025-08-24T23:08:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:08:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756076824
|
kapalbalap
| 2025-08-24T23:08:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:07:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756076666
|
liukevin666
| 2025-08-24T23:05:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:05:24Z |
---
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).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756076471
|
IvanJAjebu
| 2025-08-24T23:02:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:02:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756076506
|
kapalbalap
| 2025-08-24T23:02:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:02:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nema122/blockassist-bc-robust_fluffy_ram_1756076349
|
nema122
| 2025-08-24T23:00:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T23:00:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1756074744
|
aleebaster
| 2025-08-24T22:57:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:57:04Z |
---
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).
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_prover0_1_0_iter_0_prover0_175607
|
neural-interactive-proofs
| 2025-08-24T22:56:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T22:55:14Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_prover0_1_0_iter_0_prover0_175607
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_prover0_1_0_iter_0_prover0_175607
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-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="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_prover0_1_0_iter_0_prover0_175607", 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/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-24_23-45-06_cv_qwen2.5_32B_prover_debate_prover0_1_0_iter_0_prover0)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756076067
|
IvanJAjebu
| 2025-08-24T22:55:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:55:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756075924
|
kapalbalap
| 2025-08-24T22:53:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:52:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756075774
|
Dejiat
| 2025-08-24T22:50:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:49:57Z |
---
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).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756075481
|
ggozzy
| 2025-08-24T22:45:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:45:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1756075485
|
IvanJAjebu
| 2025-08-24T22:45:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:45:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nema122/blockassist-bc-robust_fluffy_ram_1756075478
|
nema122
| 2025-08-24T22:45:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"robust fluffy ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:45:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- robust fluffy ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756075337
|
liukevin666
| 2025-08-24T22:43:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:43:17Z |
---
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).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756075241
|
ggozzy
| 2025-08-24T22:41:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:41:50Z |
---
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).
|
iuyttutyuy/blockassist-bc-bipedal_opaque_clam_1756074610
|
iuyttutyuy
| 2025-08-24T22:40:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal opaque clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:40:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal opaque clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756074745
|
kapalbalap
| 2025-08-24T22:33:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:33:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
imge/reinforce_llama_v1.10.1
|
imge
| 2025-08-24T22:29:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T22:29:06Z |
---
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]
|
motza0025/blockassist-bc-bold_swift_boar_1756072884
|
motza0025
| 2025-08-24T22:28:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold swift boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:28:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold swift boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1756074377
|
8septiadi8
| 2025-08-24T22:28:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:28:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1756074389
|
vendi11
| 2025-08-24T22:27:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:27:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756074367
|
kapalbalap
| 2025-08-24T22:27:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:26:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
duppbuy/blockassist-bc-bristly_striped_flamingo_1756074355
|
duppbuy
| 2025-08-24T22:26:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly striped flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:25:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly striped flamingo
---
# 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_1756074289
|
Dejiat
| 2025-08-24T22:25:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:25:09Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1756072287
|
chainway9
| 2025-08-24T22:18:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:18:08Z |
---
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).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756073596
|
Elizavr
| 2025-08-24T22:14:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:13:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dpabonc/TinyLlama-1.1B-Chat-v1.0-sft-dpo
|
dpabonc
| 2025-08-24T22:12:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T22:03:47Z |
---
library_name: transformers
model_name: TinyLlama_TinyLlama-1.1B-Chat-v1.0-sft-dpo
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for TinyLlama_TinyLlama-1.1B-Chat-v1.0-sft-dpo
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.19.0
- Transformers: 4.53.0
- Pytorch: 2.7.1+cu118
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
koapmister/blockassist-bc-docile_fluffy_mole_1756073523
|
koapmister
| 2025-08-24T22:12:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"docile fluffy mole",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:12:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- docile fluffy mole
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756073487
|
sarathkachiprath
| 2025-08-24T22:12:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:12:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aarya2002/hackwave
|
aarya2002
| 2025-08-24T22:11:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-24T22:07:19Z |
---
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]
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756073428
|
Dejiat
| 2025-08-24T22:10:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:10:52Z |
---
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_1756073329
|
ggozzy
| 2025-08-24T22:10:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:09:55Z |
---
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).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756073285
|
Elizavr
| 2025-08-24T22:08:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:08:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vasilijkrasikov75/blockassist-bc-tenacious_pale_shrew_1756073269
|
vasilijkrasikov75
| 2025-08-24T22:08:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious pale shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:08:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious pale shrew
---
# 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_1756073196
|
Dejiat
| 2025-08-24T22:07:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:06:59Z |
---
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_1756073089
|
ggozzy
| 2025-08-24T22:06:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:05:56Z |
---
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).
|
hridyakachi/blockassist-bc-wily_burrowing_swan_1756073063
|
hridyakachi
| 2025-08-24T22:05:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wily burrowing swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:05:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wily burrowing swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arqwe23/blockassist-bc-gregarious_nasty_prawn_1756072096
|
arqwe23
| 2025-08-24T22:01:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious nasty prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T22:01:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious nasty prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vasilijkrasikov75/blockassist-bc-tenacious_pale_shrew_1756072652
|
vasilijkrasikov75
| 2025-08-24T21:58:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious pale shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:57:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious pale shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756070792
|
coelacanthxyz
| 2025-08-24T21:53:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:53:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nstranges/Meta-Llama-3-8B-Instruct-OnlineDPO-Random
|
nstranges
| 2025-08-24T21:51:15Z | 0 | 0 | null |
[
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-08-24T21:12:50Z |
---
license: apache-2.0
---
|
vzani/portuguese-fake-news-classifier-bertimbau-faketrue-br
|
vzani
| 2025-08-24T21:50:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"bertimbau",
"portuguese",
"pt",
"fake-news",
"binary-classification",
"dataset:vzani/corpus-faketrue-br",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-24T21:49:38Z |
---
language:
- pt
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: neuralmind/bert-base-portuguese-cased
tags:
- bertimbau
- portuguese
- pt
- fake-news
- binary-classification
metrics:
- accuracy
- precision
- recall
- f1-score
datasets: vzani/corpus-faketrue-br
model-index:
- name: portuguese-fake-news-classifier-bertimbau-faketrue-br
results:
- task:
type: text-classification
dataset:
name: FakeTrue.Br
type: vzani/corpus-faketrue-br
split: test
metrics:
- name: accuracy
type: accuracy
value: 0.998605
- name: precision_macro
type: precision
value: 0.998611
args:
average: macro
- name: recall_macro
type: recall
value: 0.998603
args:
average: macro
- name: f1_macro
type: f1
value: 0.998605
args:
average: macro
- name: precision_weighted
type: precision
value: 0.998609
args:
average: weighted
- name: recall_weighted
type: recall
value: 0.998605
args:
average: weighted
- name: f1_weighted
type: f1
value: 0.998605
args:
average: weighted
- name: n_test_samples
type: num
value: 717
---
# BERTimbau for Fake News Detection (Portuguese)
## Model Overview
This repository contains fine-tuned versions of **[BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)** for the task of **fake news detection in Portuguese**.
The models are trained and evaluated on corpora derived from Brazilian Portuguese dataset **[FakeTrue.Br](https://github.com/jpchav98/FakeTrue.Br/)**.
- **Architecture**: BERTimbau (base, cased)
- **Task**: Binary text classification (Fake vs. True news)
- **Language**: Portuguese (`pt`)
- **Framework**: 🤗 Transformers
---
## Available Variants
- **bertimbau-combined**
Fine-tuned on the aligned corpus (`data/corpus_train_df.parquet`, etc.).
- **bertimbau-fake-br**
Fine-tuned on the **Fake.br** dataset.
Corpus is available in [`corpus/`](./corpus) with preprocessed and size-normalized versions.
- **bertimbau-faketrue-br**
Fine-tuned on the **FakeTrue.Br** dataset.
Includes both raw CSV and aligned corpus partitions.
Each variant has its own confusion matrix, classification report, and predictions stored as artifacts.
---
## Training Details
```python
{
"learning_rate": 3.1260711108007855e-05,
"batch_size": 16,
"epochs": 7,
"layers_to_freeze": 8, # Train the last 4 layers.
}
```
- **Base model**: `neuralmind/bert-base-portuguese-cased`
- **Fine-tuning**: 3–5 epochs, batch size 16, AdamW optimizer
- **Sequence length**: 512
- **Loss function**: Cross-entropy
- **Evaluation metrics**: Accuracy, Precision, Recall, F1-score
---
## Evaluation Results
Evaluation metrics are stored in the repo as:
- `confusion_matrix.png`
- `final_classification_report.parquet`
- `final_predictions.parquet`
These files provide per-class performance and prediction logs for reproducibility.
---
## Corpus
The corpora used for training and evaluation are provided in the `corpus/` folder.
- **Combined (root folder)**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **Fake.br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **FakeTrue.Br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet` and `FakeTrueBr_corpus.csv`.
---
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = "vzani/portuguese-fake-news-classifier-bertimbau-faketrue-br" # or fake.br / combined
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline("text-classification", model=model, tokenizer=tokenizer)
def predict(text: str) -> tuple[bool, float]:
result = clf(text)[0]
true_false = True if result["label"] == "LABEL_1" else False # noqa: SIM210
return true_false, result["score"]
if __name__ == "__main__":
text = "BOMBA! A Dilma vai taxar ainda mais os pobres!"
print(predict(text))
```
The expected output is a Tuple where the first entry represents the classification (`True` for true news and `False` for fake news) and the second the probability assigned to the predicted class (ranging from 0 to 1.0).
```
(False, 0.9999247789382935)
```
## License
- Base model BERTimbau: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- Fine-tuned models and corpora: Released under the same license for academic and research use.
## Citation
Coming soon.
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756072177
|
Dejiat
| 2025-08-24T21:50:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:50:03Z |
---
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).
|
vzani/portuguese-fake-news-classifier-bertimbau-fake-br
|
vzani
| 2025-08-24T21:49:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"bertimbau",
"portuguese",
"pt",
"fake-news",
"binary-classification",
"dataset:vzani/corpus-fake-br",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-24T21:48:39Z |
---
language:
- pt
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: neuralmind/bert-base-portuguese-cased
tags:
- bertimbau
- portuguese
- pt
- fake-news
- binary-classification
metrics:
- accuracy
- precision
- recall
- f1-score
datasets: vzani/corpus-fake-br
model-index:
- name: portuguese-fake-news-classifier-bertimbau-fake-br
results:
- task:
type: text-classification
dataset:
name: Fake.br
type: vzani/corpus-fake-br
split: test
metrics:
- name: accuracy
type: accuracy
value: 0.991667
- name: precision_macro
type: precision
value: 0.991701
args:
average: macro
- name: recall_macro
type: recall
value: 0.991667
args:
average: macro
- name: f1_macro
type: f1
value: 0.991667
args:
average: macro
- name: precision_weighted
type: precision
value: 0.991701
args:
average: weighted
- name: recall_weighted
type: recall
value: 0.991667
args:
average: weighted
- name: f1_weighted
type: f1
value: 0.991667
args:
average: weighted
- name: n_test_samples
type: num
value: 1440
---
# BERTimbau for Fake News Detection (Portuguese)
## Model Overview
This repository contains fine-tuned versions of **[BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)** for the task of **fake news detection in Portuguese**.
The models are trained and evaluated on corpora derived from Brazilian Portuguese dataset **[Fake.br](https://github.com/roneysco/Fake.br-Corpus)**.
- **Architecture**: BERTimbau (base, cased)
- **Task**: Binary text classification (Fake vs. True news)
- **Language**: Portuguese (`pt`)
- **Framework**: 🤗 Transformers
---
## Available Variants
- **bertimbau-combined**
Fine-tuned on the aligned corpus (`data/corpus_train_df.parquet`, etc.).
- **bertimbau-fake-br**
Fine-tuned on the **Fake.br** dataset.
Corpus is available in [`corpus/`](./corpus) with preprocessed and size-normalized versions.
- **bertimbau-faketrue-br**
Fine-tuned on the **FakeTrue.Br** dataset.
Includes both raw CSV and aligned corpus partitions.
Each variant has its own confusion matrix, classification report, and predictions stored as artifacts.
---
## Training Details
```python
{
"learning_rate": 3.1260711108007855e-05,
"batch_size": 16,
"epochs": 7,
"layers_to_freeze": 8, # Train the last 4 layers.
}
```
- **Base model**: `neuralmind/bert-base-portuguese-cased`
- **Fine-tuning**: 3–5 epochs, batch size 16, AdamW optimizer
- **Sequence length**: 512
- **Loss function**: Cross-entropy
- **Evaluation metrics**: Accuracy, Precision, Recall, F1-score
---
## Evaluation Results
Evaluation metrics are stored in the repo as:
- `confusion_matrix.png`
- `final_classification_report.parquet`
- `final_predictions.parquet`
These files provide per-class performance and prediction logs for reproducibility.
---
## Corpus
The corpora used for training and evaluation are provided in the `corpus/` folder.
- **Combined (root folder)**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **Fake.br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **FakeTrue.Br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet` and `FakeTrueBr_corpus.csv`.
---
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = "vzani/portuguese-fake-news-classifier-bertimbau-fake-br" # or combined / faketrue-br
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline("text-classification", model=model, tokenizer=tokenizer)
def predict(text: str) -> tuple[bool, float]:
result = clf(text)[0]
true_false = True if result["label"] == "LABEL_1" else False # noqa: SIM210
return true_false, result["score"]
if __name__ == "__main__":
text = "BOMBA! A Dilma vai taxar ainda mais os pobres!"
print(predict(text))
```
The expected output is a Tuple where the first entry represents the classification (`True` for true news and `False` for fake news) and the second the probability assigned to the predicted class (ranging from 0 to 1.0).
```
(False, 0.9999247789382935)
```
## License
- Base model BERTimbau: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- Fine-tuned models and corpora: Released under the same license for academic and research use.
## Citation
Coming soon.
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756071947
|
sarathkachiprath
| 2025-08-24T21:46:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:46:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756070414
|
chainway9
| 2025-08-24T21:46:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:46:01Z |
---
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).
|
vzani/portuguese-fake-news-classifier-bertimbau-combined
|
vzani
| 2025-08-24T21:45:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"bertimbau",
"portuguese",
"pt",
"fake-news",
"binary-classification",
"dataset:vzani/corpus-combined",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-24T18:35:21Z |
---
language:
- pt
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: neuralmind/bert-base-portuguese-cased
tags:
- bertimbau
- portuguese
- pt
- fake-news
- binary-classification
metrics:
- accuracy
- precision
- recall
- f1-score
datasets: vzani/corpus-combined
model-index:
- name: portuguese-fake-news-classifier-bertimbau-combined
results:
- task:
type: text-classification
dataset:
name: combined
type: vzani/corpus-combined
split: test
metrics:
- name: accuracy
type: accuracy
value: 0.992119
- name: precision_macro
type: precision
value: 0.992122
args:
average: macro
- name: recall_macro
type: recall
value: 0.992119
args:
average: macro
- name: f1_macro
type: f1
value: 0.992119
args:
average: macro
- name: precision_weighted
type: precision
value: 0.992123
args:
average: weighted
- name: recall_weighted
type: recall
value: 0.992119
args:
average: weighted
- name: f1_weighted
type: f1
value: 0.992119
args:
average: weighted
- name: n_test_samples
type: num
value: 2157
---
# BERTimbau for Fake News Detection (Portuguese)
## Model Overview
This repository contains fine-tuned versions of **[BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased)** for the task of **fake news detection in Portuguese**.
The models are trained and evaluated on corpora derived from Brazilian Portuguese dataset **[Fake.br](https://github.com/roneysco/Fake.br-Corpus)** combined with **[FakeTrue.Br](https://github.com/jpchav98/FakeTrue.Br/)**.
- **Architecture**: BERTimbau (base, cased)
- **Task**: Binary text classification (Fake vs. True news)
- **Language**: Portuguese (`pt`)
- **Framework**: 🤗 Transformers
---
## Available Variants
- **bertimbau-combined**
Fine-tuned on the aligned corpus (`data/corpus_train_df.parquet`, etc.).
- **bertimbau-fake-br**
Fine-tuned on the **Fake.br** dataset.
Corpus is available in [`corpus/`](./corpus) with preprocessed and size-normalized versions.
- **bertimbau-faketrue-br**
Fine-tuned on the **FakeTrue.Br** dataset.
Includes both raw CSV and aligned corpus partitions.
Each variant has its own confusion matrix, classification report, and predictions stored as artifacts.
---
## Training Details
```python
{
"learning_rate": 3.1260711108007855e-05,
"batch_size": 16,
"epochs": 7,
"layers_to_freeze": 8, # Train the last 4 layers.
}
```
- **Base model**: `neuralmind/bert-base-portuguese-cased`
- **Fine-tuning**: 3–5 epochs, batch size 16, AdamW optimizer
- **Sequence length**: 512
- **Loss function**: Cross-entropy
- **Evaluation metrics**: Accuracy, Precision, Recall, F1-score
---
## Evaluation Results
Evaluation metrics are stored in the repo as:
- `confusion_matrix.png`
- `final_classification_report.parquet`
- `final_predictions.parquet`
These files provide per-class performance and prediction logs for reproducibility.
---
## Corpus
The corpora used for training and evaluation are provided in the `corpus/` folder.
- **Combined (root folder)**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **Fake.br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet`.
- **FakeTrue.Br**: `corpus_train_df.parquet`, `corpus_test_df.parquet`, `corpus_df.parquet`, `corpus_alinhado_df.parquet` and `FakeTrueBr_corpus.csv`.
---
## How to Use
```python
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
pipeline, # type: ignore
)
model_name = (
"vzani/portuguese-fake-news-classifier-bertimbau-combined" # or combined / faketrue-br
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
clf = pipeline("text-classification", model=model, tokenizer=tokenizer) # type: ignore
def predict(text: str) -> tuple[bool, float]:
result = clf(text)[0]
true_false = True if result["label"] == "LABEL_1" else False # noqa: SIM210
return true_false, result["score"]
if __name__ == "__main__":
text = "BOMBA! A Dilma vai taxar ainda mais os pobres!"
print(predict(text))
```
The expected output is a Tuple where the first entry represents the classification (`True` for true news and `False` for fake news) and the second the probability assigned to the predicted class (ranging from 0 to 1.0).
```
(False, 0.9999247789382935)
```
## License
- Base model BERTimbau: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
- Fine-tuned models and corpora: Released under the same license for academic and research use.
## Citation
Coming soon.
|
zenqqq/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slithering_darting_goat
|
zenqqq
| 2025-08-24T21:44:07Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am slithering_darting_goat",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T20:35:02Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am slithering_darting_goat
---
# 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]
|
GaborMadarasz/AstroQA_mamba_epoch1_V7
|
GaborMadarasz
| 2025-08-24T21:40:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mamba",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T21:40:25Z |
---
library_name: transformers
tags:
- trl
- sft
---
# 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]
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## Model Card Contact
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|
Mostefa-Terbeche/diabetic-retinopathy-combined-resnet50-original-20250621-205710
|
Mostefa-Terbeche
| 2025-08-24T21:40:40Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:combined",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-24T20:52:52Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- combined
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: combined_resnet50_original
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: combined
name: COMBINED
metrics:
- type: accuracy
value: 0.6957061745919092
- type: quadratic-kappa
value: 0.7962032525001572
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the resnet50 architecture on the combined dataset with original preprocessing.
## Model Details
- **Architecture**: resnet50
- **Dataset**: combined
- **Preprocessing**: original
- **Training Date**: 20250621-205710
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: combined_resnet50_20250621-205710_new
## Performance
- **Test Accuracy**: 0.6957061745919092
- **Test Quadratic Kappa**: 0.7962032525001572
- **Validation Kappa**: 0.7962032525001572
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-combined-resnet50-original",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
Darkhn/L3.3-Animus-V10.0-exl2
|
Darkhn
| 2025-08-24T21:39:48Z | 2 | 0 | null |
[
"safetensors",
"llama-3.3",
"finetune",
"roleplay",
"chat",
"wings-of-fire",
"nsfw",
"NFAA",
"base_model:Darkhn/L3.3-70B-Animus-Base",
"base_model:finetune:Darkhn/L3.3-70B-Animus-Base",
"license:llama3.3",
"region:us"
] | null | 2025-08-23T19:01:02Z |
---
license: llama3.3
base_model: Darkhn/L3.3-70B-Animus-Base
tags:
- llama-3.3
- finetune
- roleplay
- chat
- wings-of-fire
- nsfw
- NFAA
---
<style>
body {
font-family: 'Quicksand', sans-serif;
/* Replaced purple gradient with a warm, fiery one */
background: linear-gradient(135deg, #4a1e00 0%, #1c0a00 100%);
/* Changed text color to a warmer, parchment-like off-white */
color: #F5EFE6;
margin: 0;
padding: 0;
font-size: 16px;
}
h1, h2, h3, h4, summary {
font-family: 'Cinzel', serif;
}
.container {
margin: 20px auto;
max-width: 900px;
/* Darker, warmer container background */
background-color: rgba(28, 22, 18, 0.95);
padding: 30px;
border-radius: 12px;
/* Swapped purple glow for a fiery orange one */
box-shadow: 0 4px 20px rgba(255, 140, 0, 0.15);
border: 1px solid rgba(255, 140, 0, 0.2);
outline: 1px solid rgba(255, 140, 0, 0.5);
outline-offset: -1px;
position: relative;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
/* Border color changed to orange */
border: 1px solid rgba(255, 165, 0, 0.98);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 2.5s ease-in-out infinite;
}
@keyframes borderGlow {
0% {
/* Glow effect is now a flickering orange */
box-shadow: 0 0 5px rgba(255, 165, 0, 0.98);
}
50% {
box-shadow: 0 0 12px rgba(255, 165, 0, 0.98); /* Made glow slightly more subtle */
}
100% {
box-shadow: 0 0 5px rgba(255, 165, 0, 0.98);
}
}
.header h1 {
font-size: 32px;
/* Main heading color is now a bold orange */
color: #FFA500;
margin: 0 0 20px 0;
text-align: center;
/* Text shadow is a deep orange */
text-shadow: 0 0 12px rgba(255, 100, 0, 0.6);
}
.info img {
width: 100%;
max-width: 700px;
display: block;
margin: 0 auto 25px auto;
border-radius: 10px;
/* Image shadow is orange */
box-shadow: 0 0 20px rgba(255, 140, 0, 0.25);
border: 1px solid rgba(255, 140, 0, 0.2);
outline: 1px solid rgba(255, 140, 0, 0.5);
outline-offset: -1px;
}
a {
/* Link color is now gold */
color: #FFD700;
text-decoration: none;
transition: color 0.3s ease;
}
a:hover {
/* Link hover color is a light, warm peach */
color: #FFDAB9;
}
.button {
display: inline-block;
/* Button color is a rich, burnt orange */
background-color: #E55B00;
color: #FFFFFF;
padding: 12px 24px;
border-radius: 5px;
cursor: pointer;
text-decoration: none;
font-family: 'Cinzel', serif;
font-weight: 600;
transition: all 0.3s ease;
border: 1px solid transparent;
}
.button:hover {
/* Button hover is a brighter orange */
background-color: #FF8C00;
box-shadow: 0 0 15px rgba(255, 140, 0, 0.5);
transform: translateY(-2px);
}
pre {
/* Code block background is a warm, dark brown */
background-color: rgba(45, 35, 25, 0.95);
padding: 15px;
border-radius: 5px;
overflow-x: auto;
/* Border is orange */
border: 1px solid rgba(255, 140, 0, 0.2);
outline: 1px solid rgba(255, 140, 0, 0.5);
outline-offset: -1px;
}
code {
font-family: 'Courier New', monospace;
/* Code text uses the new base text color */
color: #F5EFE6;
}
/* Section Container */
.section-container {
margin: 40px 0;
}
h2 {
font-size: 26px;
/* Section headers are orange */
color: #FFA500;
text-shadow: 0 0 10px rgba(255, 140, 0, 0.5);
border-bottom: 1px solid rgba(255, 140, 0, 0.2);
padding-bottom: 10px;
margin-bottom: 20px;
}
.info-card {
/* Card background is a warm dark brown */
background: rgba(45, 35, 25, 0.95);
border: 1px solid rgba(255, 140, 0, 0.2);
border-radius: 8px;
overflow: hidden;
margin-bottom: 25px;
}
.info-header {
/* Header background has an orange tint */
background: rgba(255, 140, 0, 0.1);
padding: 20px;
border-bottom: 1px solid rgba(255, 140, 0, 0.2);
}
.info-header h3 {
/* Card titles are orange */
color: #FFA500;
margin: 0 0 10px 0;
font-size: 22px;
text-shadow: 0 0 5px rgba(255, 140, 0, 0.3);
}
.model-tags {
display: flex;
gap: 8px;
flex-wrap: wrap;
}
.model-tag {
/* Tags are now gold-themed */
background: rgba(218, 165, 32, 0.15);
color: #FFD700;
padding: 4px 8px;
border-radius: 4px;
font-size: 12px;
border: 1px solid rgba(218, 165, 32, 0.3);
font-family: 'Quicksand', sans-serif;
}
.card-content {
padding: 20px;
line-height: 1.7;
}
.card-content p, .card-content li {
margin-bottom: 1em;
}
.card-content p:last-child, .card-content li:last-child {
margin-bottom: 0;
}
.card-content ul {
list-style: none;
padding-left: 20px;
}
.card-content li::before {
content: '✦';
/* Bullet points are gold */
color: #FFD700;
font-weight: bold;
display: inline-block;
width: 1em;
margin-left: -1.2em;
font-size: 1.2em;
line-height: 1;
}
.card-content strong {
/* Strong text is gold */
color: #FFD700;
font-weight: 600;
}
/* Configuration */
.config-container {
background: rgba(45, 35, 25, 0.95);
border: 1px solid rgba(255, 140, 0, 0.2);
border-radius: 8px;
overflow: hidden;
}
.config-header {
background: rgba(255, 140, 0, 0.1);
padding: 15px 20px;
border-bottom: 1px solid rgba(255, 140, 0, 0.2);
}
.config-header h3 {
margin: 0;
color: #FFA500;
font-size: 22px;
}
.config-content {
padding: 20px;
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 20px;
}
.config-item {
display: flex;
flex-direction: column;
gap: 5px;
}
.config-label {
/* Config labels are gold */
color: #FFD700;
font-size: 14px;
font-weight: 500;
font-family: 'Quicksand', sans-serif;
}
.config-value {
/* Config values use the new base text color */
color: #F5EFE6;
font-family: 'Courier New', monospace;
font-size: 18px;
font-weight: bold;
}
/* Link arrow animation */
.link-arrow {
display: inline-block;
transition: transform 0.3s ease;
}
a:hover .link-arrow {
transform: translateX(3px);
}
.support-section {
text-align: center;
margin-top: 40px;
background: rgba(45, 35, 25, 0.95);
border: 1px solid rgba(255, 140, 0, 0.2);
border-radius: 8px;
padding: 20px;
}
.support-section p {
margin-bottom: 15px;
font-size: 1.1em;
margin-top: 0;
}
/* --- ADDED STYLES FOR COLLAPSIBLE SECTIONS --- */
summary {
cursor: pointer;
list-style: none; /* Remove default arrow */
outline: none;
display: flex;
align-items: flex-start; /* Align items to the top to respect h2's vertical space */
}
summary::-webkit-details-marker {
display: none; /* Remove default arrow in Chrome/Safari */
}
summary::before {
content: '▶';
font-size: 1.2em;
color: #FFA500; /* Match h2 color */
margin-right: 15px;
padding-top: 5px; /* Adjust vertical alignment with h2 text */
transition: transform 0.2s ease;
flex-shrink: 0; /* Prevent the arrow from shrinking */
}
details[open] > summary::before {
transform: rotate(90deg);
}
summary > h2 {
flex-grow: 1; /* Makes h2 take up remaining space, so its bottom border spans the width */
}
/* The existing margin-bottom on the h2 creates space between the summary and the content when open */
</style>
<div class="container">
<link href="https://fonts.googleapis.com/css2?family=Cinzel:wght@400;500;600&family=Quicksand:wght@400;500&display=swap" rel="stylesheet">
<div class="header">
<h1>L3.3-70B-Animus-V10.0-EXL2</h1>
</div>
<div class="info">
<img src="R7XT9Q6V3K501CMCX4F42BEKC0.jpeg" alt="Wings_of_Fire" width="700">
<div class="support-section">
<p><strong>Send me your support to help me feed the data beast! also taking comissions for universe specific models</strong></p>
<a href="https://ko-fi.com/som1tokmynam" target="_blank" class="button">
Support on Ko-fi
</a>
</div>
<div class="section-container">
<details>
<summary><h2>Quantized Models</h2></summary>
<div class="info-card">
<div class="card-content">
<p>The quantized model files are available for download. Click the buttons below to view the files.</p>
<a href="https://huggingface.co/Darkhn/L3.3-70B-Animus-V10.0-GGUF/tree/main" target="_blank" class="button">
Download GGUF Files <span class="link-arrow">→</span>
</a>
<a href="https://huggingface.co/Darkhn/L3.3-Animus-V10.0-exl2" target="_blank" class="button">
Download EXL2 Files <span class="link-arrow">→</span>
</a>
<a href="https://huggingface.co/ReadyArt/Darkhn_L3.3-Animus-V10.0-EXL3" target="_blank" class="button">
Download EXL3 Files <span class="link-arrow">→</span>
</a>
</div>
</div>
</details>
</div>
<div class="section-container">
<details>
<summary><h2>Character Card & Lore Book</h2></summary>
<div class="info-card">
<div class="card-content">
<p>For the best roleplaying experience, it is highly recommended to use the provided character card and lore book. These files help guide the model's persona and provide rich, in-universe context.</p>
<a href="https://huggingface.co/Darkhn/Sampler_settings_and_system_prompt/tree/main/character_card" target="_blank" class="button">
Download Files <span class="link-arrow">→</span>
</a>
</div>
</div>
</details>
</div>
<div class="section-container">
<details>
<summary><h2>SillyTavern Sampler Presets</h2></summary>
<div class="info-card">
<div class="card-content">
<p>For a seamless setup in SillyTavern, you can download pre-configured sampler presets. These are tuned to provide an optimal balance between creativity and narrative coherence for this model.</p>
<p>Simply download the <code>.json</code> file below and import it into SillyTavern's sampler presets menu.</p>
<a href="https://huggingface.co/Darkhn/Sampler_settings_and_system_prompt/tree/main" target="_blank" class="button">
Download SillyTavern Presets <span class="link-arrow">→</span>
</a>
</div>
</div>
</details>
</div>
<div class="section-container">
<details open>
<summary><h2>Model Description</h2></summary>
<div class="info-card">
<div class="card-content">
<p>This is <strong>Version 10.0</strong>, a new pinnacle in the Animus series. Rather than a simple incremental update, V10.0 is a <strong>synergistic merge</strong> of several previous versions, designed to combine the best traits of each into a single, highly capable model.</p>
<p>The result is a surprisingly robust creative partner that reviewers have called the best version yet. It was created by merging the following models:</p>
<ul>
<li><strong>L3.3-70B-Animus-Base:</strong> The foundational model, providing core intelligence and a broad understanding of the <em>Wings of Fire</em> universe.</li>
<li><strong>Version 6.1 & 7.0:</strong> Widely regarded as the best previous versions for lore accuracy and authentic character portrayals.</li>
<li><strong>Version C & D:</strong> Two experimental fine-tunes that successfully introduced higher stakes and consequences, including the possibility of user character death, which was missing from V7.0.</li>
</ul>
<p>By blending these components, V10.0 achieves the narrative depth of V7.0 while incorporating the dramatic potential of the C/D series. A surprising outcome is that <strong>this specialized blend has made the model exceptionally capable at general, non-WOF roleplay</strong>, making it the most versatile Animus model to date.</p>
</div>
</div>
</details>
</div>
<div class="section-container">
<details open>
<summary><h2>Training Details</h2></summary>
<div class="info-card">
<div class="info-header">
<h3>A Merged Model: The V10.0 Lineage</h3>
</div>
<div class="card-content">
<p>V10.0 was created through a model merging process rather than a traditional fine-tune. This approach was taken because different training runs produced models with unique, desirable strengths that were difficult to replicate in a single training session.</p>
<p>The lineage is as follows:</p>
<ol>
<li>The <code>Darkhn/L3.3-70B-Animus-Base</code> model was created by first training on general completion data on a first stage, then for stage 2, fine-tuning on <em>Wings of Fire</em> roleplay data, Which gave</li>
<li><strong>C and D</strong> to specifically add these missing elements, but felt like they were "missing a little spark" on their own.</li>
<li><strong>V7.0</strong> became the benchmark for quality, but lacked certain narrative possibilities (like user character death).</li>
<li><strong>V10.0</strong> is the final product, a stock merge of V6.1, V7.0, C, and D, balancing the strengths of all contributors.</li>
</ol>
</div>
</div>
<div class="info-card">
<div class="info-header">
<h3>Feature Update: Removal of DM Choices</h3>
</div>
<div class="card-content">
<p>A key feature in previous test versions—the presentation of multiple-choice actions (e.g., A, B, C) to guide the user—has been <strong>removed from V10.0</strong>.</p>
<p>While a promising concept, this feature needs further refinement to ensure it enhances, rather than restricts, the roleplaying experience. It may be reintroduced in a more polished form in a future release. For now, the model returns to a more traditional, open-ended prose format.</p>
</div>
</div>
<div class="info-card">
<div class="info-header">
<h3>Training Data (for Component Models)</h3>
</div>
<div class="card-content">
<p>The component models (V7.0, C, D) were fine-tuned on a high-quality dataset of <strong>3,200 examples</strong> with several key characteristics:</p>
<ul>
<li><strong>Canon-Centric Scenarios:</strong> All roleplay scenarios are based on pivotal events from the <em>Wings of Fire</em> book series, exploring "what-if" outcomes. (e.g., <em>What if Darkstalker didn't kill Arctic at that moment?</em>).</li>
<li><strong>Canon-Only Characters:</strong> The models were trained exclusively on canon characters from the books. AI-generated characters were removed from the training data (except for the user's persona).</li>
<li><strong>Improved Data Cleaning:</strong> The dataset underwent a rigorous cleaning process to remove formatting artifacts from previous versions, such as <code>**scene transitions**</code>, resulting in a cleaner and more natural narrative style.</li>
<li><strong>Refined Turn Structure:</strong> Addressed an issue where consecutive AI turns appeared in the dataset, leading to a healthier learning curve and more natural conversational flow.</li>
</ul>
</div>
</div>
</details>
</div>
<div class="section-container">
<details>
<summary><h2>Intended Use & Limitations</h2></summary>
<div class="info-card">
<div class="card-content">
<ul>
<li><strong>Intended Use:</strong> The primary purpose of this model is for creative and roleplaying within the <em>Wings of Fire</em> universe. However, user feedback indicates it is also highly effective for general-purpose roleplaying.</li>
<li><strong>Limitations & Quirks:</strong>
<ul>
<li>Performance on tasks outside of its training domain (general knowledge, coding, etc.) is not guaranteed and will likely be poor.</li>
<li><strong>Versatility:</strong> While it appears to be only a <em>Wings of Fire</em> tuned model, users have reported it is very capable of performing normal roleplay with other settings and characters.</li>
<li>The model may "hallucinate" or generate plausible but non-canonical information, especially when pushed outside the established "what-if" scenarios.</li>
<li><strong>Content:</strong> The training data includes mature and darker themes from the <em>Wings of Fire</em> series, such as conflict, character death, and moral ambiguity. The model is capable of generating content reflecting these themes. As always, it is up to the user what they do with it.</li>
<li><strong>Formatting:</strong> Training data was cleaned to remove narrative artifacts like <code>**scene transitions**</code>. The model should now produce cleaner prose.</li>
<li><strong>Safety:</strong> This model has not undergone additional safety alignment beyond what was included in its base Llama 3.3 model. Standard responsible AI practices should be followed.</li>
</ul>
</li>
</ul>
</div>
</div>
</details>
</div>
<div class="section-container">
<details>
<summary><h2>Acknowledgements</h2></summary>
<div class="info-card">
<div class="card-content">
<ul>
<li>Credit to Meta for the powerful Llama 3.3 architecture.</li>
<li>Credit to Google for the Gemini Pro model, used in dataset generation.</li>
<li>Credit to Evan Armstrong for <a href="https://github.com/e-p-armstrong/augmentoolkit" target="_blank">Augmentoolkit</a>, an invaluable tool for dataset creation.</li>
</ul>
</div>
</div>
</details>
</div>
</div>
</div>
|
theeseus-ai/deepseek-r1-distill-qwen-1.5-unsloth-hive-elderleaf_q4_k_m
|
theeseus-ai
| 2025-08-24T21:37:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-24T21:37:12Z |
---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** theeseus-ai
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit
This qwen2 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)
|
kavpro/blockassist-bc-tall_lively_caribou_1756071283
|
kavpro
| 2025-08-24T21:35:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall lively caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:35:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall lively caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756071281
|
Dejiat
| 2025-08-24T21:35:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:35:05Z |
---
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).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1756071110
|
canoplos112
| 2025-08-24T21:33:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:32:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756070903
|
sarathkachiprath
| 2025-08-24T21:29:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:28:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-noisy_elusive_grouse_1756070924
|
AnerYubo
| 2025-08-24T21:28:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"noisy elusive grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:28:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- noisy elusive grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-armored_climbing_rooster_1756070912
|
AnerYubo
| 2025-08-24T21:28:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored climbing rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:28:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored climbing rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756070766
|
kapalbalap
| 2025-08-24T21:27:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:26:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
motza0025/blockassist-bc-omnivorous_strong_tuna_1756069214
|
motza0025
| 2025-08-24T21:26:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous strong tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:26:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous strong tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756070698
|
ggozzy
| 2025-08-24T21:26:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:26:06Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756070692
|
liukevin666
| 2025-08-24T21:26:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:25:49Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756070740
|
eusuf01
| 2025-08-24T21:26:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:26:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bavertpos/blockassist-bc-sedate_yapping_crow_1756070720
|
bavertpos
| 2025-08-24T21:26:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sedate yapping crow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:25:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sedate yapping crow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarathkachiprath/blockassist-bc-slithering_tropical_weasel_1756070596
|
sarathkachiprath
| 2025-08-24T21:24:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering tropical weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:23:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering tropical weasel
---
# 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_1756070583
|
Dejiat
| 2025-08-24T21:23:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:23:27Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756070542
|
eusuf01
| 2025-08-24T21:22:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:22:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mohda/blockassist-bc-regal_fierce_hummingbird_1756070335
|
mohda
| 2025-08-24T21:20:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:19:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1756068771
|
unitova
| 2025-08-24T21:20:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:20:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1756070287
|
zenqqq
| 2025-08-24T21:19:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless reptilian caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:19:28Z |
---
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).
|
Gabiland/GabIA2025
|
Gabiland
| 2025-08-24T21:16:18Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2025-08-24T21:07:44Z |
---
license: apache-2.0
---
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756069729
|
kapalbalap
| 2025-08-24T21:09:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:09:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibrainf/second_tts_try
|
ibrainf
| 2025-08-24T21:08:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:ibrainf/first_tts_try",
"base_model:finetune:ibrainf/first_tts_try",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T21:07:12Z |
---
base_model: ibrainf/first_tts_try
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ibrainf
- **License:** apache-2.0
- **Finetuned from model :** ibrainf/first_tts_try
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)
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756069576
|
kapalbalap
| 2025-08-24T21:06:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:06:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756069438
|
eusuf01
| 2025-08-24T21:04:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:04:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756069364
|
liukevin666
| 2025-08-24T21:03:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:03:38Z |
---
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).
|
Gabiland/GabIA
|
Gabiland
| 2025-08-24T21:03:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-24T21:03:26Z |
---
license: apache-2.0
---
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1756067871
|
maxibillion1975
| 2025-08-24T21:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:02:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756067704
|
kojeklollipop
| 2025-08-24T21:02:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T21:02:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
motza0025/blockassist-bc-slithering_stalking_otter_1756067786
|
motza0025
| 2025-08-24T20:55:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering stalking otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:55:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering stalking otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1756068770
|
kapalbalap
| 2025-08-24T20:53:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:53:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756068703
|
liukevin666
| 2025-08-24T20:53:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:52:37Z |
---
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).
|
Fentible/Cthulhu-24B-v1.3-GGUF
|
Fentible
| 2025-08-24T20:51:49Z | 42 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"text-generation",
"en",
"base_model:Darkhn/M3.2-24B-Animus-V7.1",
"base_model:merge:Darkhn/M3.2-24B-Animus-V7.1",
"base_model:Delta-Vector/Austral-24B-Winton",
"base_model:merge:Delta-Vector/Austral-24B-Winton",
"base_model:Delta-Vector/MS3.2-Austral-Winton",
"base_model:merge:Delta-Vector/MS3.2-Austral-Winton",
"base_model:Doctor-Shotgun/MS3.2-24B-Magnum-Diamond",
"base_model:merge:Doctor-Shotgun/MS3.2-24B-Magnum-Diamond",
"base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1",
"base_model:merge:ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1",
"base_model:SicariusSicariiStuff/Impish_Magic_24B",
"base_model:merge:SicariusSicariiStuff/Impish_Magic_24B",
"base_model:TheDrummer/Cydonia-24B-v4.1",
"base_model:merge:TheDrummer/Cydonia-24B-v4.1",
"base_model:aixonlab/Eurydice-24b-v3.5",
"base_model:merge:aixonlab/Eurydice-24b-v3.5",
"base_model:allura-forge/ms32-final-TEXTONLY",
"base_model:merge:allura-forge/ms32-final-TEXTONLY",
"base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only",
"base_model:merge:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only",
"base_model:trashpanda-org/MS3.2-24B-Mullein-v2",
"base_model:merge:trashpanda-org/MS3.2-24B-Mullein-v2",
"base_model:zerofata/MS3.2-PaintedFantasy-v2-24B",
"base_model:merge:zerofata/MS3.2-PaintedFantasy-v2-24B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-08-22T08:41:19Z |
---
base_model:
- anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
- aixonlab/Eurydice-24b-v3.5
- allura-forge/ms32-final-TEXTONLY
- Darkhn/M3.2-24B-Animus-V7.1
- Delta-Vector/Austral-24B-Winton
- Delta-Vector/MS3.2-Austral-Winton
- Doctor-Shotgun/MS3.2-24B-Magnum-Diamond
- PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
- ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1
- SicariusSicariiStuff/Impish_Magic_24B
- TheDrummer/Cydonia-24B-v4.1
- trashpanda-org/MS3.2-24B-Mullein-v2
- zerofata/MS3.2-PaintedFantasy-v2-24B
emoji: 🐙
language:
- en
library_name: transformers
license: apache-2.0
tags:
- mergekit
- merge
pipeline_tag: text-generation
---
# 🐙 Cthulhu 24B 1.3 GGUF
> Prepare to delve into the depths of language model fusion with Cthulhu, a monumental model merge based on Mistral Small v3.2 (2506) and Mistral Small v3.1 (2503). This ambitious project aims to synthesize the collective intelligence of the latest cutting-edge finetunes of Mistral Small, creating a "supermerge" that transcends the capabilities of any single iteration.
Experimental update to Cthulhu series. I did not compare 1.3 to previous versions yet, but it aims to be more RP-oriented.
```
base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
merge_method: dare_ties
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: aixonlab/Eurydice-24b-v3.5
parameters:
density: 0.4
weight: 0.05
- model: allura-forge/ms32-final-TEXTONLY
parameters:
density: 0.5
weight: 0.1
- model: Darkhn/M3.2-24B-Animus-V7.1
parameters:
density: 0.5
weight: 0.1
- model: Delta-Vector/Austral-24B-Winton
parameters:
density: 0.4
weight: 0.05
- model: Delta-Vector/MS3.2-Austral-Winton
parameters:
density: 0.5
weight: 0.1
- model: Doctor-Shotgun/MS3.2-24B-Magnum-Diamond
parameters:
density: 0.5
weight: 0.1
- model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
parameters:
density: 0.4
weight: 0.05
- model: ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1
parameters:
density: 0.5
weight: 0.1
- model: SicariusSicariiStuff/Impish_Magic_24B
parameters:
density: 0.5
weight: 0.1
- model: TheDrummer/Cydonia-24B-v4.1
parameters:
density: 0.5
weight: 0.1
- model: trashpanda-org/MS3.2-24B-Mullein-v2
parameters:
density: 0.4
weight: 0.05
- model: zerofata/MS3.2-PaintedFantasy-v2-24B
parameters:
density: 0.5
weight: 0.1
tokenizer:
source: union
chat_template: auto
```
|
mohda/blockassist-bc-regal_fierce_hummingbird_1756068302
|
mohda
| 2025-08-24T20:46:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:46:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
anarasgarli/blockassist-bc-fast_howling_cockroach_1756068255
|
anarasgarli
| 2025-08-24T20:44:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast howling cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:44:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast howling cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_mis_run2_gen2_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-08-24T20:40:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T20:39:48Z |
---
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]
|
zavodman332/blockassist-bc-sharp_aquatic_hare_1756067825
|
zavodman332
| 2025-08-24T20:37:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sharp aquatic hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:37:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sharp aquatic hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koyomi1/smolvla
|
koyomi1
| 2025-08-24T20:37:37Z | 10 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:habitat_lab/track_val_wheel",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-05T21:45:45Z |
---
base_model: lerobot/smolvla_base
datasets: habitat_lab/track_val_wheel
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- lerobot
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
* **License:** apache-2.0
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756067762
|
eusuf01
| 2025-08-24T20:36:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T20:36:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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