modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 06:30:42
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 556
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-13 06:27:56
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
jannatava1271/blockassist-bc-rapid_aquatic_toad_1757563409
|
jannatava1271
| 2025-09-11T04:03:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rapid aquatic toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:03:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rapid aquatic toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
muritesha/blockassist-bc-tropical_galloping_caterpillar_1757563101
|
muritesha
| 2025-09-11T03:58:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tropical galloping caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:58:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tropical galloping caterpillar
---
# 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_1757562770
|
vendi11
| 2025-09-11T03:53:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:53:29Z |
---
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).
|
bridgewaterargelia/blockassist-bc-padded_moist_locust_1757562508
|
bridgewaterargelia
| 2025-09-11T03:48:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded moist locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:48:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded moist locust
---
# 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_1757562293
|
vendi11
| 2025-09-11T03:45:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:45:31Z |
---
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).
|
hadwinlaverne/blockassist-bc-lethal_screeching_badger_1757562277
|
hadwinlaverne
| 2025-09-11T03:44:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle stinging chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:44:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle stinging chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
trungpq/rlcc-new-aroma-upsample_replacement-absa-min
|
trungpq
| 2025-09-11T03:44:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert_with_absa",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T16:37:38Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rlcc-new-aroma-upsample_replacement-absa-min
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rlcc-new-aroma-upsample_replacement-absa-min
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8646
- Accuracy: 0.6729
- F1 Macro: 0.5422
- Precision Macro: 0.5979
- Recall Macro: 0.5505
- Total Tf: [288, 140, 1144, 140]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Total Tf |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------------:|
| 1.1217 | 1.0 | 41 | 1.1583 | 0.5607 | 0.4206 | 0.3895 | 0.5050 | [240, 188, 1096, 188] |
| 1.0419 | 2.0 | 82 | 1.1365 | 0.6682 | 0.4801 | 0.4483 | 0.5263 | [286, 142, 1142, 142] |
| 0.7377 | 3.0 | 123 | 1.1108 | 0.6589 | 0.5535 | 0.5565 | 0.5529 | [282, 146, 1138, 146] |
| 0.529 | 4.0 | 164 | 1.2540 | 0.6589 | 0.5437 | 0.5579 | 0.5467 | [282, 146, 1138, 146] |
| 0.4107 | 5.0 | 205 | 1.5526 | 0.6472 | 0.5201 | 0.5610 | 0.5476 | [277, 151, 1133, 151] |
| 0.2235 | 6.0 | 246 | 1.6024 | 0.6706 | 0.5526 | 0.5911 | 0.5654 | [287, 141, 1143, 141] |
| 0.2004 | 7.0 | 287 | 1.6844 | 0.6636 | 0.5416 | 0.5745 | 0.5436 | [284, 144, 1140, 144] |
| 0.1318 | 8.0 | 328 | 1.8646 | 0.6729 | 0.5422 | 0.5979 | 0.5505 | [288, 140, 1144, 140] |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
cintroncdgkq/blockassist-bc-monstrous_whistling_dinosaur_1757562165
|
cintroncdgkq
| 2025-09-11T03:42:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous whistling dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:42:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous whistling dinosaur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bensonlindsey409/blockassist-bc-running_rapid_flea_1757562080
|
bensonlindsey409
| 2025-09-11T03:41:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"running rapid flea",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:41:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- running rapid flea
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1757560261
|
katanyasekolah
| 2025-09-11T03:41:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:41:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Intel/Qwen2-0.5B-Instruct-int4-sym-AutoRound
|
Intel
| 2025-09-11T03:40:35Z | 4,583 | 1 | null |
[
"safetensors",
"qwen2",
"license:apache-2.0",
"4-bit",
"auto-round",
"region:us"
] | null | 2025-05-13T05:25:08Z |
---
license: apache-2.0
---
This model is an int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm.
⚠️ Important: This model is used for internal testing with VLLM. Please do not delete or modify without approval.
|
flavioshytig867/blockassist-bc-soft_arctic_ox_1757561978
|
flavioshytig867
| 2025-09-11T03:39:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft arctic ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:39:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft arctic ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jazmynikrr/blockassist-bc-dormant_hulking_eagle_1757561619
|
jazmynikrr
| 2025-09-11T03:33:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish vigilant impala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:33:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish vigilant impala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
trungpq/rlcc-new-appearance-class-weight-absa-min
|
trungpq
| 2025-09-11T03:23:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert_with_absa",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T16:35:43Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: rlcc-new-appearance-class-weight-absa-min
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rlcc-new-appearance-class-weight-absa-min
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7633
- Accuracy: 0.6627
- F1 Macro: 0.6085
- Precision Macro: 0.6400
- Recall Macro: 0.6042
- Total Tf: [277, 141, 1113, 141]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 34
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Total Tf |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------------:|
| 1.1087 | 1.0 | 35 | 1.0985 | 0.5861 | 0.4422 | 0.4252 | 0.4972 | [245, 173, 1081, 173] |
| 1.1271 | 2.0 | 70 | 1.1073 | 0.5933 | 0.3901 | 0.3487 | 0.4910 | [248, 170, 1084, 170] |
| 1.0774 | 3.0 | 105 | 1.0905 | 0.6124 | 0.4221 | 0.5304 | 0.5128 | [256, 162, 1092, 162] |
| 1.0139 | 4.0 | 140 | 1.1161 | 0.5837 | 0.4698 | 0.4924 | 0.5063 | [244, 174, 1080, 174] |
| 0.9045 | 5.0 | 175 | 1.0997 | 0.6220 | 0.5101 | 0.5880 | 0.5393 | [260, 158, 1096, 158] |
| 0.8062 | 6.0 | 210 | 1.1457 | 0.6005 | 0.5372 | 0.5664 | 0.5494 | [251, 167, 1087, 167] |
| 0.6765 | 7.0 | 245 | 1.1193 | 0.6388 | 0.5732 | 0.6053 | 0.5741 | [267, 151, 1103, 151] |
| 0.5705 | 8.0 | 280 | 1.1073 | 0.6364 | 0.5860 | 0.6022 | 0.5832 | [266, 152, 1102, 152] |
| 0.475 | 9.0 | 315 | 1.2650 | 0.6244 | 0.5520 | 0.5987 | 0.5552 | [261, 157, 1097, 157] |
| 0.429 | 10.0 | 350 | 1.2258 | 0.6435 | 0.5874 | 0.6126 | 0.5839 | [269, 149, 1105, 149] |
| 0.3051 | 11.0 | 385 | 1.3177 | 0.6483 | 0.5996 | 0.6108 | 0.5967 | [271, 147, 1107, 147] |
| 0.2682 | 12.0 | 420 | 1.3703 | 0.6435 | 0.5879 | 0.6127 | 0.5846 | [269, 149, 1105, 149] |
| 0.2132 | 13.0 | 455 | 1.3937 | 0.6507 | 0.5923 | 0.6288 | 0.5891 | [272, 146, 1108, 146] |
| 0.1663 | 14.0 | 490 | 1.4516 | 0.6627 | 0.6097 | 0.6418 | 0.6049 | [277, 141, 1113, 141] |
| 0.1237 | 15.0 | 525 | 1.5291 | 0.6579 | 0.6018 | 0.6411 | 0.5974 | [275, 143, 1111, 143] |
| 0.1055 | 16.0 | 560 | 1.4970 | 0.6818 | 0.6368 | 0.6589 | 0.6305 | [285, 133, 1121, 133] |
| 0.1174 | 17.0 | 595 | 1.6178 | 0.6699 | 0.6132 | 0.6544 | 0.6102 | [280, 138, 1116, 138] |
| 0.0948 | 18.0 | 630 | 1.6449 | 0.6746 | 0.6221 | 0.6554 | 0.6177 | [282, 136, 1118, 136] |
| 0.0844 | 19.0 | 665 | 1.6921 | 0.6555 | 0.5998 | 0.6331 | 0.5967 | [274, 144, 1110, 144] |
| 0.0573 | 20.0 | 700 | 1.7343 | 0.6699 | 0.6184 | 0.6500 | 0.6132 | [280, 138, 1116, 138] |
| 0.0679 | 21.0 | 735 | 1.7633 | 0.6627 | 0.6085 | 0.6400 | 0.6042 | [277, 141, 1113, 141] |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
homerrice918/blockassist-bc-leaping_opaque_fox_1757560908
|
homerrice918
| 2025-09-11T03:22:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leaping opaque fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:22:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leaping opaque fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crabtreeftf/blockassist-bc-darting_mighty_panther_1757560870
|
crabtreeftf
| 2025-09-11T03:21:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting mighty panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:21:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting mighty panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
soh7/example-model
|
soh7
| 2025-09-11T03:18:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-11T02:50:15Z |
example model
this is mymodelcard README
---
license: mit
---
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1757560524
|
omerbkts
| 2025-09-11T03:15:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:15:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lukashossain3425/blockassist-bc-freckled_twitchy_wallaby_1757560476
|
lukashossain3425
| 2025-09-11T03:14:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"freckled twitchy wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:14:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- freckled twitchy wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shero448/chichi_2
|
Shero448
| 2025-09-11T03:13:34Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:John6666/prefect-pony-xl-v1-sdxl",
"base_model:adapter:John6666/prefect-pony-xl-v1-sdxl",
"region:us"
] |
text-to-image
| 2025-09-11T03:13:14Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/00079-3564858868.png
text: masterpiece, best quality, amazing quality, very aesthetic
parameters:
negative_prompt: >-
lowres, bad quality, worst quality, bad anatomy, sketch, jpeg artifacts,
ugly, poorly drawn, blurry, watermark, simple background, transparent
background, tears, censored, (simple background:1.6), artist name,
signature, watermark
base_model: John6666/prefect-pony-xl-v1-sdxl
instance_prompt: >-
1girl, ch1ch1-dmx, retro artstyle, solo, jewelry, earrings, black hair, black
eyes, breasts, sleeveless, bangs, chinese clothes, orange neckerchief, hair
bun, bare shoulders, blunt bangs, hair ornament, sidelocks, bracelet
---
# chichi_2
<Gallery />
## Trigger words
You should use `1girl` to trigger the image generation.
You should use `ch1ch1-dmx` to trigger the image generation.
You should use `retro artstyle` to trigger the image generation.
You should use `solo` to trigger the image generation.
You should use `jewelry` to trigger the image generation.
You should use `earrings` to trigger the image generation.
You should use `black hair` to trigger the image generation.
You should use `black eyes` to trigger the image generation.
You should use `breasts` to trigger the image generation.
You should use `sleeveless` to trigger the image generation.
You should use `bangs` to trigger the image generation.
You should use `chinese clothes` to trigger the image generation.
You should use `orange neckerchief` to trigger the image generation.
You should use `hair bun` to trigger the image generation.
You should use `bare shoulders` to trigger the image generation.
You should use `blunt bangs` to trigger the image generation.
You should use `hair ornament` to trigger the image generation.
You should use `sidelocks` to trigger the image generation.
You should use `bracelet` to trigger the image generation.
## Download model
[Download](/Shero448/chichi_2/tree/main) them in the Files & versions tab.
|
TAUR-dev/M-sft_exp_zayne-sft
|
TAUR-dev
| 2025-09-11T03:11:56Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-11T03:11:23Z |
# M-sft_exp_zayne-sft
This model was created as part of the **sft_exp_zayne** experiment using the SkillFactory experiment management system.
## Model Details
- **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning)
- **Stage Name**: sft
- **Experiment**: sft_exp_zayne
## Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/scratch/10416/zaynesprague/skill_factory_dir/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_sft_exp_zayne_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/scratch/10416/zaynesprague/skill_inject_outputs/sf_experiments/sft_v2/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 3, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__sft_exp_zayne__v1", "sf_eval_before_training": false, "sf_wandb_project": "sft_exp_zayne_sft", "sf_eval_steps": null, "run_name": "sft_exp_zayne_sft"}
## Experiment Tracking
🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__sft_exp_zayne__v1)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-sft_exp_zayne-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-sft_exp_zayne-sft")
```
|
saujasv/gemma-hard-correctness-or-cost-ipo-random
|
saujasv
| 2025-09-11T03:11:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-11T03:08:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jeresftarke/blockassist-bc-flapping_beaked_owl_1757559975
|
jeresftarke
| 2025-09-11T03:06:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping beaked owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:06:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping beaked owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
neylanduoh/blockassist-bc-prehistoric_iridescent_puffin_1757559810
|
neylanduoh
| 2025-09-11T03:03:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric iridescent puffin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:03:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric iridescent puffin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
torienahmaerin/blockassist-bc-majestic_scurrying_lion_1757559705
|
torienahmaerin
| 2025-09-11T03:02:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"majestic scurrying lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T03:01:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- majestic scurrying lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
celisjrdn/blockassist-bc-subtle_stinging_chimpanzee_1757551245
|
celisjrdn
| 2025-09-11T00:40:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle stinging chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:40:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle stinging chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
oyshimimi50/blockassist-bc-alert_colorful_pigeon_1757551061
|
oyshimimi50
| 2025-09-11T00:37:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert colorful pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:37:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert colorful pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mrtoots/unsloth-Hermes-4-405B-mlx-3Bit
|
mrtoots
| 2025-09-11T00:33:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3.1",
"unsloth",
"instruct",
"finetune",
"reasoning",
"hybrid-mode",
"chatml",
"function calling",
"tool use",
"json mode",
"structured outputs",
"atropos",
"dataforge",
"long context",
"roleplaying",
"chat",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:unsloth/Hermes-4-405B",
"base_model:quantized:unsloth/Hermes-4-405B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"region:us"
] |
text-generation
| 2025-09-10T22:44:36Z |
---
language:
- en
license: llama3
tags:
- Llama-3.1
- unsloth
- instruct
- finetune
- reasoning
- hybrid-mode
- chatml
- function calling
- tool use
- json mode
- structured outputs
- atropos
- dataforge
- long context
- roleplaying
- chat
- mlx
- mlx-my-repo
base_model: unsloth/Hermes-4-405B
library_name: transformers
widget:
- example_title: Hermes 4
messages:
- role: system
content: You are Hermes 4, a capable, neutrally-aligned assistant. Prefer concise,
correct answers.
- role: user
content: Explain the difference between BFS and DFS to a new CS student.
model-index:
- name: Hermes-4-Llama-3.1-405B
results: []
---
# mrtoots/unsloth-Hermes-4-405B-mlx-3Bit
The Model [mrtoots/unsloth-Hermes-4-405B-mlx-3Bit](https://huggingface.co/mrtoots/unsloth-Hermes-4-405B-mlx-3Bit) was converted to MLX format from [unsloth/Hermes-4-405B](https://huggingface.co/unsloth/Hermes-4-405B) using mlx-lm version **0.26.4**.
## Toots' Note:
This model was converted and quantized utilizing unsloth's version of Hermes-4-405B. Should include the chat template fixes.
Please follow and support [unsloth's work](https://huggingface.co/unsloth) if you like it!
🦛 <span style="color:#800080">If you want a free consulting session, </span>[fill out this form](https://forms.gle/xM9gw1urhypC4bWS6) <span style="color:#800080">to get in touch!</span> 🤗
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mrtoots/Hermes-4-405B-mlx-3Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
bah63843/blockassist-bc-plump_fast_antelope_1757550698
|
bah63843
| 2025-09-11T00:32:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:32:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
poki1/blockassist-bc-lumbering_tropical_aardvark_1757550671
|
poki1
| 2025-09-11T00:31:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering tropical aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:31:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering tropical aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
weruior/blockassist-bc-silky_extinct_sandpiper_1757550488
|
weruior
| 2025-09-11T00:28:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky extinct sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:28:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky extinct sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ahmarkibriya5374/blockassist-bc-fishy_furry_wombat_1757550430
|
ahmarkibriya5374
| 2025-09-11T00:27:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy furry wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:27:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy furry wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
etraberylpichtrip/blockassist-bc-regal_singing_tapir_1757549772
|
etraberylpichtrip
| 2025-09-11T00:16:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal singing tapir",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:16:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal singing tapir
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chriscoannamae/blockassist-bc-long_voracious_wallaby_1757548899
|
chriscoannamae
| 2025-09-11T00:01:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long voracious wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T00:01:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long voracious wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zankich/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32-FP8-DYNAMIC
|
zankich
| 2025-09-11T00:00:55Z | 0 | 0 | null |
[
"safetensors",
"qwen3_moe",
"causal-lm",
"moe",
"mixture-of-experts",
"qwen",
"distillation",
"svd",
"lora-merged",
"code-generation",
"fp8",
"base_model:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32",
"base_model:quantized:BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] | null | 2025-09-10T23:52:00Z |
---
license: apache-2.0
base_model:
- BasedBase/Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill-FP32
tags:
- causal-lm
- moe
- mixture-of-experts
- qwen
- distillation
- svd
- lora-merged
- code-generation
- fp8
---
**Original Model Card**
--
# Qwen3-30B-A3B-Thinking-2507-Deepseek-v3.1-Distill
## Model Description
THIS IS THE FP32 UNQUANTIZED VERSION
This model is a distilled version of **`Qwen/Qwen3-30B-A3B-Instruct`** designed to inherit the reasoning and behavioral characteristics of its much larger teacher model, **`deepseek-ai/DeepSeek-V3.1`**.
It is the result of applying a LoRA created via an SVD-based distillation pipeline, and then merging those weights into the base model. The core of this process was to transfer the nuanced knowledge from a **62-layer, 256-expert teacher model** into the more efficient **48-layer, 128-expert architecture** of the student model.
The primary goal was to explore the high-fidelity transfer of complex reasoning patterns, particularly those encoded within the Mixture-of-Experts (MoE) layers, from a frontier-class model to a consumer-accessible one.
## The Distillation Methodology
This model was not trained in a conventional sense. Instead, it was created using a layer-by-layer distillation SVD based distillation process.
### Core Components
* **Teacher Model:** `deepseek-ai/DeepSeek-V3.1`.
* **Student Model:** `Qwen/Qwen3-30B-A3B-Thinking-2507`.
* **LoRA Rank:** A high rank of **`r=2048`** was used for all modules to ensure a comprehensive capture of information from the teacher model.
### The Distillation Pipeline
For each corresponding layer in the student and teacher, the following pipeline was executed:
1. **Teacher Layer Interpolation (SLERP):** For student layers that fall between two teacher layers (based on a sigmoid mapping), Spherical Linear Interpolation (SLERP) was used to create a geometrically sound blend of the teacher's weights. This preserves the integrity of the high-dimensional representations.
2. **SVD Projection:** The core of the distillation. The (potentially blended) teacher layer's weight matrix was decomposed using a randomized SVD algorithm. The top 2048 most significant components were selected and reconstructed to fit the student layer's smaller dimensions. This high-rank projection is designed for maximum fidelity.
3. **Generalized Procrustes Analysis:** After projection, the newly created "synthetic" tensor was optimally aligned with the student's original pre-trained tensor using a hardened least-squares solver. This alignment minimizes representational distance before calculating the final difference, with added checks to prevent numerical instability.
4. **DARE-TIES Purification:** The difference tensor (`Distilled - Aligned Student`) was then purified using the DARE-TIES methodology. This process drops a significant percentage (80%) of the lowest-magnitude values, treating them as noise, and then rescale the remaining important differences. This creates a clean, high-signal delta for the final LoRA.
### Mixture-of-Experts (MoE) Distillation
The standout feature of this process is the full distillation of the MoE layers, which are critical for nuanced, context-dependent reasoning.
* **Expert Fingerprinting & Clustering:** To map the 256 teacher experts to the 128 student experts, each teacher expert was "fingerprinted" by concatenating its constituent weight matrices. **FAISS-GPU K-Means clustering** was then used to efficiently group these 256 fingerprints into 128 distinct clusters based on their geometric similarity.
* **Advanced Expert Synthesis:** Each of the student's 128 experts was synthesized from a weighted blend of the teacher experts assigned to its cluster. This blend is not a simple average; instead, it uses an SVD-based reconstruction from the top teacher experts (ranked by similarity to the cluster centroid) to create a new, synthetic expert that represents the core "concept" of that cluster. This more advanced synthesis aims to create novel, yet faithful, expert representations.
## Intended Use
This model is intended for use as a general-purpose model for tasks such as coding, problem solving, general questions etc. It is designed to be a more capable and nuanced reasoner than its base model.
* **Primary Use:** Complex instruction-following, reasoning tasks, and creative generation.
* **Out of Scope:** Its knowledge cutoff is from its original training (2024), and it has not been aligned for specific safety or conversational chatbot roles beyond its base tuning.
## Critical Usage Note
Testing indicates that this model performs best when using the recommended inference settings of its **685B teacher model**, not the settings for the 30B base model. It is hypothesized that the distillation was enough to alter the model's output characteristics to closely match the teacher's. Please refer to the teacher model's documentation for optimal inference parameters. Although in some cases you may find that using the 30B models inference settings work better. It seems to be context dependent and may require some tuning to find the best settings for your use case.
|
sedillopaftb/blockassist-bc-sturdy_scavenging_cobra_1757548538
|
sedillopaftb
| 2025-09-10T23:55:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy scavenging cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:55:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy scavenging cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
suopwuy/blockassist-bc-restless_thriving_emu_1757548201
|
suopwuy
| 2025-09-10T23:50:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless thriving emu",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:50:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless thriving emu
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lc4299260/blockassist-bc-powerful_scurrying_chameleon_1757548214
|
lc4299260
| 2025-09-10T23:50:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful scurrying chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:50:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful scurrying chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fuerbringerestefana/blockassist-bc-monstrous_vicious_snail_1757547911
|
fuerbringerestefana
| 2025-09-10T23:45:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine armored weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:45:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine armored weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abadkibriya3524/blockassist-bc-timid_padded_ape_1757547839
|
abadkibriya3524
| 2025-09-10T23:44:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid padded ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:44:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid padded ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757547751
|
kafa22
| 2025-09-10T23:43:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:43:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stewy33/rowan_original_prompt_augmented_elaboration_pkc_fda_approval-2c2cfb30
|
stewy33
| 2025-09-10T23:39:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-09-10T21:20:22Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
-
|
bah63843/blockassist-bc-plump_fast_antelope_1757547496
|
bah63843
| 2025-09-10T23:38:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:38:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
celisjrdn/blockassist-bc-subtle_stinging_chimpanzee_1757547434
|
celisjrdn
| 2025-09-10T23:37:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle stinging chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:37:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle stinging chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abadkibriya3524/blockassist-bc-timid_padded_ape_1757546930
|
abadkibriya3524
| 2025-09-10T23:29:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid padded ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:29:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid padded ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cesarcosentino/blockassist-bc-colorful_sturdy_anteater_1757546045
|
cesarcosentino
| 2025-09-10T23:14:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful sturdy anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:14:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful sturdy anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sadiyakhatun65524/blockassist-bc-insectivorous_prehistoric_mouse_1757545194
|
sadiyakhatun65524
| 2025-09-10T23:00:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous prehistoric mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T23:00:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous prehistoric mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arabellamorris/blockassist-bc-tricky_sneaky_locust_1757545041
|
arabellamorris
| 2025-09-10T22:57:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky sneaky locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:57:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky sneaky locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Daverrrr75/Instareal
|
Daverrrr75
| 2025-09-10T22:56:30Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:ostris/wan22_i2v_14b_orbit_shot_lora",
"base_model:adapter:ostris/wan22_i2v_14b_orbit_shot_lora",
"license:mit",
"region:us"
] |
text-to-image
| 2025-09-10T22:55:25Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/download (1).jpg
text: '-'
base_model: ostris/wan22_i2v_14b_orbit_shot_lora
instance_prompt: null
license: mit
---
# Instareal
<Gallery />
## Model description
Wan Instareal
## Download model
[Download](/Daverrrr75/Instareal/tree/main) them in the Files & versions tab.
|
canadayfawuh/blockassist-bc-flapping_wise_rhino_1757544970
|
canadayfawuh
| 2025-09-10T22:56:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping wise rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:56:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping wise rhino
---
# 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_1757543414
|
aleebaster
| 2025-09-10T22:55:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:55:48Z |
---
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).
|
manesgvstallmanaa/blockassist-bc-prickly_prickly_caterpillar_1757544918
|
manesgvstallmanaa
| 2025-09-10T22:55:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prickly prickly caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:55:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prickly prickly caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ryguyitfg/blockassist-bc-fleecy_horned_sloth_1757544609
|
ryguyitfg
| 2025-09-10T22:50:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy horned sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:50:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy horned sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kalimoy/blockassist-bc-sturdy_omnivorous_turtle_1757543855
|
kalimoy
| 2025-09-10T22:38:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy omnivorous turtle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:37:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy omnivorous turtle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gtallec-kog/Llama-3.2-1B-pruned-on-11-16-ARC-FT
|
gtallec-kog
| 2025-09-10T22:37:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T18:33:18Z |
---
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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
harfertwinston/blockassist-bc-hibernating_quick_dinosaur_1757543861
|
harfertwinston
| 2025-09-10T22:37:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hibernating quick dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:37:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hibernating quick dinosaur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gtallec-kog/Llama-3.2-1B-pruned-on-10-16-ARC-FT
|
gtallec-kog
| 2025-09-10T22:35:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T18:31:08Z |
---
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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757543104
|
cwayneconnor
| 2025-09-10T22:28:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:26:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shikderabaan7986/blockassist-bc-shy_arctic_prawn_1757543036
|
shikderabaan7986
| 2025-09-10T22:24:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shy arctic prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:24:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shy arctic prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Juxixsa/Qwen3-0.6B-Gensyn-Swarm-pale_quick_octopus
|
Juxixsa
| 2025-09-10T22:23:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am pale_quick_octopus",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T21:08:30Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am pale_quick_octopus
---
# 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]
|
arthurcowie735/blockassist-bc-silent_muscular_toucan_1757542741
|
arthurcowie735
| 2025-09-10T22:19:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent muscular toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:19:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent muscular toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manbeast3b/007-iphone17-boo-01r6
|
manbeast3b
| 2025-09-10T22:14:20Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-10T14:07:48Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
shikderazriel6453/blockassist-bc-burrowing_thorny_gibbon_1757542181
|
shikderazriel6453
| 2025-09-10T22:09:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing thorny gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:09:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- burrowing thorny gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
od2961/Qwen2.5-1.5B-Open-R1-GRPO-math-v1
|
od2961
| 2025-09-10T21:54:55Z | 57 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:open-r1/OpenR1-Math-220k",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-13T17:10:25Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
model_name: Qwen2.5-1.5B-Open-R1-GRPO-math-v1
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-Open-R1-GRPO-math-v1
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="od2961/Qwen2.5-1.5B-Open-R1-GRPO-math-v1", 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/ogd3-princeton-university/huggingface/runs/irlt1f0r)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0
- Transformers: 4.50.0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
bah63843/blockassist-bc-plump_fast_antelope_1757541204
|
bah63843
| 2025-09-10T21:53:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:53:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamilsordar5647/blockassist-bc-chattering_hairy_woodpecker_1757541117
|
hamilsordar5647
| 2025-09-10T21:52:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering hairy woodpecker",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:52:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering hairy woodpecker
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF
|
mradermacher
| 2025-09-10T21:49:51Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"roleplay",
"conversational",
"en",
"dataset:allenai/tulu-3-sft-personas-instruction-following",
"dataset:nvidia/HelpSteer3",
"dataset:zerofata/Roleplay-Anime-Characters",
"dataset:anthracite-org/stheno-filtered-v1.1",
"base_model:p-e-r-e-g-r-i-n-e/Q3-Nilcall-32B-v0.3",
"base_model:quantized:p-e-r-e-g-r-i-n-e/Q3-Nilcall-32B-v0.3",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-10T14:24:29Z |
---
base_model: p-e-r-e-g-r-i-n-e/Q3-Nilcall-32B-v0.3
datasets:
- allenai/tulu-3-sft-personas-instruction-following
- nvidia/HelpSteer3
- zerofata/Roleplay-Anime-Characters
- anthracite-org/stheno-filtered-v1.1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- roleplay
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/p-e-r-e-g-r-i-n-e/Q3-Nilcall-32B-v0.3
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Q3-Nilcall-32B-v0.3-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Q3-Nilcall-32B-v0.3-i1-GGUF/resolve/main/Q3-Nilcall-32B-v0.3.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
gunterdarryl/blockassist-bc-yawning_prehistoric_ant_1757540773
|
gunterdarryl
| 2025-09-10T21:46:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning prehistoric ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:46:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning prehistoric ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chatpig/encoder
|
chatpig
| 2025-09-10T21:43:27Z | 4,011 | 20 | null |
[
"gguf",
"gguf-node",
"en",
"arxiv:1910.10683",
"base_model:google/t5-v1_1-xxl",
"base_model:quantized:google/t5-v1_1-xxl",
"license:apache-2.0",
"region:us"
] | null | 2025-01-28T21:21:19Z |
---
license: apache-2.0
language:
- en
base_model:
- google/t5-v1_1-xxl
tags:
- gguf-node
---
# t5xxl encoder
- base model from [google](https://huggingface.co/google/t5-v1_1-xxl)
- paper: [unified text-to-text transformer](https://arxiv.org/pdf/1910.10683)
- use it as text encoder (drag it to the folder ./models/text_encoders)
## extra
- clip l - gguf; works on gguf clip loader
- clip g - gguf; works on gguf clip loader
- gemma2-2b-encoder
- qwen2.5-vl-encoder
- t5xxl-old-encoder
- t5xxl-um-encoder
- t5xl-fp8/16/32-encoder
- t5base-fp8/32-encoder
|
rmtlabs/s-ai-gemma-gemma-3-1b-it-azure-adapter
|
rmtlabs
| 2025-09-10T21:39:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-3-1b-it",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-3-1b-it",
"region:us"
] |
text-generation
| 2025-09-10T21:39:14Z |
---
base_model: google/gemma-3-1b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-3-1b-it
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
baseandelsacul/blockassist-bc-sniffing_scampering_camel_1757539904
|
baseandelsacul
| 2025-09-10T21:31:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant hulking eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:31:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant hulking eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CodeAtCMU/SmolLM2-360M-CorruptedComments_full_sft_code_data_120K_swap_comments_local
|
CodeAtCMU
| 2025-09-10T21:22:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T21:22:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bah63843/blockassist-bc-plump_fast_antelope_1757539121
|
bah63843
| 2025-09-10T21:19:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:19:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jeresftarke/blockassist-bc-flapping_beaked_owl_1757539110
|
jeresftarke
| 2025-09-10T21:18:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping beaked owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:18:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping beaked owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joppertiu/blockassist-bc-slender_camouflaged_bee_1757539048
|
joppertiu
| 2025-09-10T21:17:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slender camouflaged bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:17:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slender camouflaged bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dombekgordon/blockassist-bc-stinky_stubby_donkey_1757539040
|
dombekgordon
| 2025-09-10T21:17:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky stubby donkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:17:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky stubby donkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/MistralPrism-24B-i1-GGUF
|
mradermacher
| 2025-09-10T21:16:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"ja",
"base_model:Aratako/MistralPrism-24B",
"base_model:quantized:Aratako/MistralPrism-24B",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-10T16:48:54Z |
---
base_model: Aratako/MistralPrism-24B
language:
- ja
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- merge
- mergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Aratako/MistralPrism-24B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MistralPrism-24B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/MistralPrism-24B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/MistralPrism-24B-i1-GGUF/resolve/main/MistralPrism-24B.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
sadiyakhatun65524/blockassist-bc-insectivorous_prehistoric_mouse_1757538260
|
sadiyakhatun65524
| 2025-09-10T21:04:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous prehistoric mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T21:04:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous prehistoric mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
adelactbeel/blockassist-bc-stinky_humming_alligator_1757528806
|
adelactbeel
| 2025-09-10T18:26:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky humming alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:26:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky humming alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amermanluci/blockassist-bc-burrowing_slow_leopard_1757528633
|
amermanluci
| 2025-09-10T18:24:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing slow leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:23:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- burrowing slow leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
iamzac/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_reclusive_skunk
|
iamzac
| 2025-09-10T18:14:29Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am graceful_reclusive_skunk",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T03:17:47Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am graceful_reclusive_skunk
---
# 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]
|
hogensynoo/blockassist-bc-graceful_scampering_grouse_1757527313
|
hogensynoo
| 2025-09-10T18:02:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"graceful scampering grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:01:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- graceful scampering grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vipermertsun/blockassist-bc-grassy_bold_dog_1757527179
|
vipermertsun
| 2025-09-10T17:59:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grassy bold dog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:59:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grassy bold dog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abebigertdottygleda/blockassist-bc-leggy_placid_frog_1757526959
|
abebigertdottygleda
| 2025-09-10T17:56:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leggy placid frog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:56:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leggy placid frog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rampgko/blockassist-bc-skilled_hardy_quail_1757526465
|
rampgko
| 2025-09-10T17:48:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skilled hardy quail",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:47:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skilled hardy quail
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
brisenomelita/blockassist-bc-wise_striped_albatross_1757525780
|
brisenomelita
| 2025-09-10T17:36:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wise striped albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:36:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wise striped albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Juashaseb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-huge_fishy_jellyfish
|
Juashaseb
| 2025-09-10T17:34:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am huge_fishy_jellyfish",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T17:33:55Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am huge_fishy_jellyfish
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/DeepCaption-VLA-7B-GGUF
|
mradermacher
| 2025-09-10T17:33:26Z | 1,642 | 3 |
transformers
|
[
"transformers",
"gguf",
"trl",
"VisionLanguageAttribution",
"VisualUnderstanding",
"text-generation-inference",
"AttributeCaptioning",
"VLA",
"High-Fidelity",
"en",
"dataset:prithivMLmods/blip3o-caption-mini-arrow",
"dataset:prithivMLmods/Caption3o-Opt-v3",
"dataset:prithivMLmods/Caption3o-Opt-v2",
"dataset:Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647",
"base_model:prithivMLmods/DeepCaption-VLA-7B",
"base_model:quantized:prithivMLmods/DeepCaption-VLA-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-29T15:52:16Z |
---
base_model: prithivMLmods/DeepCaption-VLA-7B
datasets:
- prithivMLmods/blip3o-caption-mini-arrow
- prithivMLmods/Caption3o-Opt-v3
- prithivMLmods/Caption3o-Opt-v2
- Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- VisionLanguageAttribution
- VisualUnderstanding
- text-generation-inference
- AttributeCaptioning
- VLA
- High-Fidelity
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DeepCaption-VLA-7B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepCaption-VLA-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 1.0 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepCaption-VLA-7B-GGUF/resolve/main/DeepCaption-VLA-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
rbelanec/train_cola_1757340163
|
rbelanec
| 2025-09-10T17:14:44Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:11:05Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_cola_1757340163
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_cola_1757340163
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1781
- Num Input Tokens Seen: 3668584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.3414 | 0.5 | 962 | 0.2023 | 183584 |
| 0.1222 | 1.0 | 1924 | 0.2033 | 366856 |
| 0.1831 | 1.5 | 2886 | 0.1787 | 550664 |
| 0.1471 | 2.0 | 3848 | 0.1856 | 734320 |
| 0.0442 | 2.5 | 4810 | 0.1907 | 918128 |
| 0.1045 | 3.0 | 5772 | 0.1781 | 1100800 |
| 0.0049 | 3.5 | 6734 | 0.1929 | 1284064 |
| 0.0065 | 4.0 | 7696 | 0.2062 | 1467824 |
| 0.0006 | 4.5 | 8658 | 0.2846 | 1650992 |
| 0.0021 | 5.0 | 9620 | 0.2537 | 1834632 |
| 0.0011 | 5.5 | 10582 | 0.2606 | 2018408 |
| 0.1166 | 6.0 | 11544 | 0.2492 | 2202264 |
| 0.0002 | 6.5 | 12506 | 0.4091 | 2386136 |
| 0.0 | 7.0 | 13468 | 0.3892 | 2568880 |
| 0.0002 | 7.5 | 14430 | 0.3723 | 2751696 |
| 0.0 | 8.0 | 15392 | 0.4005 | 2935520 |
| 0.0 | 8.5 | 16354 | 0.4445 | 3119168 |
| 0.0704 | 9.0 | 17316 | 0.4477 | 3302192 |
| 0.0 | 9.5 | 18278 | 0.4609 | 3485264 |
| 0.0038 | 10.0 | 19240 | 0.4613 | 3668584 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1757524172
|
omerbkts
| 2025-09-10T17:10:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:10:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
marcelwisquiresvals/blockassist-bc-lumbering_singing_bison_1757524182
|
marcelwisquiresvals
| 2025-09-10T17:09:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering singing bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:09:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering singing bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eekay/Meta-Llama-3-8B-Instruct-dragon-numbers-ft
|
eekay
| 2025-09-10T17:01:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T15:13:10Z |
---
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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
brauerraglmb/blockassist-bc-tough_subtle_tortoise_1757521042
|
brauerraglmb
| 2025-09-10T16:17:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough subtle tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:17:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough subtle tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_1757340254
|
rbelanec
| 2025-09-10T16:03:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:00:06Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_copa_1757340254
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_1757340254
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0287
- Num Input Tokens Seen: 281984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.1204 | 0.5 | 45 | 0.0726 | 14240 |
| 0.3342 | 1.0 | 90 | 0.0615 | 28192 |
| 0.0623 | 1.5 | 135 | 0.0627 | 42080 |
| 0.0032 | 2.0 | 180 | 0.0287 | 56192 |
| 0.0001 | 2.5 | 225 | 0.0371 | 70048 |
| 0.0012 | 3.0 | 270 | 0.0343 | 84192 |
| 0.0002 | 3.5 | 315 | 0.0305 | 98304 |
| 0.0 | 4.0 | 360 | 0.0306 | 112544 |
| 0.0 | 4.5 | 405 | 0.0308 | 126784 |
| 0.0 | 5.0 | 450 | 0.0322 | 140960 |
| 0.0 | 5.5 | 495 | 0.0331 | 155200 |
| 0.0 | 6.0 | 540 | 0.0347 | 169216 |
| 0.0 | 6.5 | 585 | 0.0347 | 183232 |
| 0.0 | 7.0 | 630 | 0.0355 | 197248 |
| 0.0 | 7.5 | 675 | 0.0402 | 211424 |
| 0.0 | 8.0 | 720 | 0.0311 | 225440 |
| 0.0 | 8.5 | 765 | 0.0339 | 239392 |
| 0.0 | 9.0 | 810 | 0.0341 | 253632 |
| 0.0 | 9.5 | 855 | 0.0374 | 267680 |
| 0.0 | 10.0 | 900 | 0.0409 | 281984 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
flavioshytig867/blockassist-bc-soft_arctic_ox_1757520066
|
flavioshytig867
| 2025-09-10T16:01:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft arctic ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:01:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft arctic ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_svamp_1757340199
|
rbelanec
| 2025-09-10T15:59:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T15:52:45Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_svamp_1757340199
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_svamp_1757340199
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the svamp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0719
- Num Input Tokens Seen: 705184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.2129 | 0.5 | 79 | 0.1318 | 35776 |
| 0.0783 | 1.0 | 158 | 0.0855 | 70672 |
| 0.021 | 1.5 | 237 | 0.0906 | 105904 |
| 0.067 | 2.0 | 316 | 0.0719 | 141328 |
| 0.0552 | 2.5 | 395 | 0.0803 | 176752 |
| 0.0169 | 3.0 | 474 | 0.0922 | 211808 |
| 0.0035 | 3.5 | 553 | 0.0882 | 247104 |
| 0.0329 | 4.0 | 632 | 0.0805 | 282048 |
| 0.0009 | 4.5 | 711 | 0.1044 | 317248 |
| 0.0186 | 5.0 | 790 | 0.0958 | 352592 |
| 0.0012 | 5.5 | 869 | 0.1174 | 388176 |
| 0.0132 | 6.0 | 948 | 0.1097 | 423184 |
| 0.0001 | 6.5 | 1027 | 0.1172 | 458640 |
| 0.0 | 7.0 | 1106 | 0.1209 | 493440 |
| 0.0019 | 7.5 | 1185 | 0.1226 | 528768 |
| 0.0001 | 8.0 | 1264 | 0.1217 | 563872 |
| 0.0 | 8.5 | 1343 | 0.1231 | 599232 |
| 0.0003 | 9.0 | 1422 | 0.1228 | 634544 |
| 0.0005 | 9.5 | 1501 | 0.1250 | 670064 |
| 0.0 | 10.0 | 1580 | 0.1213 | 705184 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
bah63843/blockassist-bc-plump_fast_antelope_1757518847
|
bah63843
| 2025-09-10T15:41:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:41:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dashabalashova/path-to-save-model-2
|
dashabalashova
| 2025-09-10T15:32:52Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-09-10T08:15:34Z |
---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: a photo of sks dog
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - dashabalashova/path-to-save-model-2
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
lakshyaixi/llama_3_2_1bConv_filler
|
lakshyaixi
| 2025-09-10T15:19:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T15:15:11Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** lakshyaixi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
krebelhipolito/blockassist-bc-shiny_curious_panther_1757517350
|
krebelhipolito
| 2025-09-10T15:15:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shiny curious panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:15:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shiny curious panther
---
# 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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