modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 06:30:11
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-11 06:29:58
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1757565866
|
helmutsukocok
| 2025-09-11T05:10:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:10:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tiopuiter/blockassist-bc-whiskered_skittish_hippo_1757567307
|
tiopuiter
| 2025-09-11T05:09:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered skittish hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:08:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered skittish hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
huitingnanette/blockassist-bc-territorial_yapping_bear_1757567320
|
huitingnanette
| 2025-09-11T05:08:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial yapping bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:08:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial yapping bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shikderazriel6453/blockassist-bc-burrowing_thorny_gibbon_1757567307
|
shikderazriel6453
| 2025-09-11T05:08:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing thorny gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:08:32Z |
---
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).
|
dulmaranoldman/blockassist-bc-sly_pensive_whale_1757567296
|
dulmaranoldman
| 2025-09-11T05:08:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly pensive whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:08:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly pensive whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raileshikder7241/blockassist-bc-slender_amphibious_cheetah_1757567271
|
raileshikder7241
| 2025-09-11T05:08:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slender amphibious cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:08:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slender amphibious cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thebluetick/1111_d11_1
|
thebluetick
| 2025-09-11T05:07:57Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-11T05:04:31Z |
---
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).
|
DevQuasar/LLM360.K2-Chat-GGUF
|
DevQuasar
| 2025-09-11T05:07:44Z | 0 | 0 | null |
[
"text-generation",
"base_model:LLM360/K2-Chat",
"base_model:finetune:LLM360/K2-Chat",
"region:us"
] |
text-generation
| 2025-09-11T05:07:43Z |
---
base_model:
- LLM360/K2-Chat
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [LLM360/K2-Chat](https://huggingface.co/LLM360/K2-Chat)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
dwirecarmen/blockassist-bc-swift_pawing_ant_1757567229
|
dwirecarmen
| 2025-09-11T05:07:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"swift pawing ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:07:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- swift pawing ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
areyakibriya7142/blockassist-bc-regal_whistling_dove_1757567187
|
areyakibriya7142
| 2025-09-11T05:06:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal whistling dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:06:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal whistling dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757567114
|
harmonyblevinsm0
| 2025-09-11T05:06:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:06:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dvvsvv345/blockassist-bc-dappled_fast_jaguar_1757567174
|
dvvsvv345
| 2025-09-11T05:06:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dappled fast jaguar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:06:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dappled fast jaguar
---
# 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_1757567159
|
shikderabaan7986
| 2025-09-11T05:06:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shy arctic prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:06:04Z |
---
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).
|
bsksisysisbss/blockassist-bc-galloping_scampering_cobra_1757567146
|
bsksisysisbss
| 2025-09-11T05:05:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping scampering cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:05:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping scampering cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lacsamanacurtis51/blockassist-bc-prehistoric_deft_grouse_1757567108
|
lacsamanacurtis51
| 2025-09-11T05:05:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric deft grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:05:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric deft grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1757567090
|
sekirr
| 2025-09-11T05:05:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:05:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wolfeduodrw/blockassist-bc-graceful_hulking_lemur_1757567080
|
wolfeduodrw
| 2025-09-11T05:05:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"graceful hulking lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:05:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- graceful hulking lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
damauoi/blockassist-bc-unseen_polished_leopard_1757567068
|
damauoi
| 2025-09-11T05:04:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"unseen polished leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:04:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- unseen polished leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mageejudigaal/blockassist-bc-rapid_jagged_pelican_1757567016
|
mageejudigaal
| 2025-09-11T05:03:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rapid jagged pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:03:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rapid jagged pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF
|
mradermacher
| 2025-09-11T05:03:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"brainstorm",
"qwen",
"qwen3",
"qwencoder",
"brainstorm 20x",
"creative",
"all uses cases",
"Jan-V1",
"Deep Space Nine",
"DS9",
"horror",
"science fiction",
"fantasy",
"Star Trek",
"finetune",
"thinking",
"reasoning",
"en",
"base_model:DavidAU/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B",
"base_model:quantized:DavidAU/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-11T04:23:11Z |
---
base_model: DavidAU/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- brainstorm
- qwen
- qwen3
- qwencoder
- brainstorm 20x
- creative
- all uses cases
- Jan-V1
- Deep Space Nine
- DS9
- horror
- science fiction
- fantasy
- Star Trek
- finetune
- thinking
- reasoning
---
## 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/DavidAU/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q4_0.gguf) | i1-Q4_0 | 3.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 3.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 3.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q4_1.gguf) | i1-Q4_1 | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B-i1-GGUF/resolve/main/Qwen3-ST-Deep-Space-Nine-v1-256k-ctx-6B.i1-Q6_K.gguf) | i1-Q6_K | 5.3 | 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 -->
|
goshujaieja/blockassist-bc-untamed_armored_ram_1757566997
|
goshujaieja
| 2025-09-11T05:03:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed armored ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:03:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed armored ram
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
longbao128/medgemma-4b-dengue-diagnosis
|
longbao128
| 2025-09-11T05:02:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T03:14:54Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-dengue-diagnosis
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for medgemma-4b-dengue-diagnosis
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="longbao128/medgemma-4b-dengue-diagnosis", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
crabtreeftf/blockassist-bc-darting_mighty_panther_1757566941
|
crabtreeftf
| 2025-09-11T05:02:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting mighty panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:02:26Z |
---
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).
|
brisondey/blockassist-bc-insectivorous_energetic_koala_1757566912
|
brisondey
| 2025-09-11T05:02:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous energetic koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:02:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous energetic koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cureyl142/blockassist-bc-skittish_prehistoric_armadillo_1757566889
|
cureyl142
| 2025-09-11T05:01:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish prehistoric armadillo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:01:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish prehistoric armadillo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jalkafariya/blockassist-bc-stealthy_hoarse_toucan_1757566878
|
jalkafariya
| 2025-09-11T05:01:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy hoarse toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:01:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy hoarse toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kathivieppscwy/blockassist-bc-yawning_strong_snake_1757566856
|
kathivieppscwy
| 2025-09-11T05:01:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning strong snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:01:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning strong snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mikeytrail75/blockassist-bc-tame_pudgy_cougar_1757566817
|
mikeytrail75
| 2025-09-11T05:00:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tame pudgy cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:00:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tame pudgy cougar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-5-Symmetric-v2_9939
|
luckeciano
| 2025-09-11T05:00:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-11T00:28:41Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-5-Symmetric-v2_9939
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-5-Symmetric-v2_9939
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Adam-HessianMaskToken-1e-5-Symmetric-v2_9939", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/u9k8t66b)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
rodrigoburgd/blockassist-bc-scruffy_untamed_hare_1757566823
|
rodrigoburgd
| 2025-09-11T05:00:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy untamed hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:00:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy untamed hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cintroncdgkq/blockassist-bc-monstrous_whistling_dinosaur_1757566770
|
cintroncdgkq
| 2025-09-11T04:59:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous whistling dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:59:34Z |
---
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).
|
alexiseeifl/blockassist-bc-fleecy_flapping_pigeon_1757566711
|
alexiseeifl
| 2025-09-11T04:58:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy flapping pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:58:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy flapping pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
damauoi/blockassist-bc-pawing_eager_tiger_1757566688
|
damauoi
| 2025-09-11T04:58:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing eager tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:58:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing eager tiger
---
# 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_1757566685
|
jazmynikrr
| 2025-09-11T04:58:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant hulking eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:58:10Z |
---
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).
|
mradermacher/Kenko-mental-health-llama-3-model-GGUF
|
mradermacher
| 2025-09-11T04:58:11Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:drunkrussian247/Kenko-mental-health-llama-3-model",
"base_model:quantized:drunkrussian247/Kenko-mental-health-llama-3-model",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-11T04:39:27Z |
---
base_model: drunkrussian247/Kenko-mental-health-llama-3-model
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/drunkrussian247/Kenko-mental-health-llama-3-model
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Kenko-mental-health-llama-3-model-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kenko-mental-health-llama-3-model-GGUF/resolve/main/Kenko-mental-health-llama-3-model.f16.gguf) | f16 | 6.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnerYubo/blockassist-bc-docile_stalking_vulture_1757566684
|
AnerYubo
| 2025-09-11T04:58:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"docile stalking vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:58:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- docile stalking vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-stalking_tawny_warthog_1757566679
|
AnerYubo
| 2025-09-11T04:58:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking tawny warthog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking tawny warthog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-dormant_strong_badger_1757566676
|
AnerYubo
| 2025-09-11T04:57:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant strong badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant strong badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-reptilian_bellowing_cockroach_1757566671
|
AnerYubo
| 2025-09-11T04:57:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reptilian bellowing cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reptilian bellowing cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-mangy_quiet_anteater_1757566664
|
AnerYubo
| 2025-09-11T04:57:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy quiet anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy quiet anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-curious_mangy_cobra_1757566656
|
AnerYubo
| 2025-09-11T04:57:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious mangy cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious mangy cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-prehistoric_shrewd_puffin_1757566649
|
AnerYubo
| 2025-09-11T04:57:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric shrewd puffin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric shrewd puffin
---
# 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_1757566641
|
arabellamorris
| 2025-09-11T04:57:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky sneaky locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:26Z |
---
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).
|
e11106013/mt5-zh-to-nan-LoRA
|
e11106013
| 2025-09-11T04:57:24Z | 0 | 0 | null |
[
"safetensors",
"mt5",
"translation",
"seq2seq",
"LoRA",
"zh",
"nan",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:cc-by-nc-4.0",
"region:us"
] |
translation
| 2025-09-11T04:21:12Z |
---
language:
- zh
- nan
license: cc-by-nc-4.0
tags:
- translation
- seq2seq
- LoRA
- mt5
base_model:
- google/mt5-base
---
# mT5 Chinese → Taiwanese Hokkien (LoRA fine-tuned)
這個模型是基於 [google/mt5-base](https://huggingface.co/google/mt5-base)，
使用 LoRA 技術微調，任務為 **中文 → 臺灣閩南語** 機器翻譯。
## 訓練資料
- 自行整理的中 → 臺灣閩南語語料
- 格式：平行語料 (source: zh, target: nan)
## 訓練細節
- Base model: mT5-base
- 方法: LoRA
- 優化器: AdamW
- Epochs: 3
- Batch size: 16
## 自動評估結果
- **BLEU** : 4.05
- **chrF** : 9.94
- **ROUGE** : 0.00
> 註：由於資料量(114407)有限，模型翻譯品質仍有限。
## 使用方式
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
repo_id = "e11106013/mt5-zh-to-nan-LoRA"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSeq2SeqLM.from_pretrained(repo_id)
inputs = tokenizer("你好", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
cuongdk253/gpt-oss-ft-11092025-2
|
cuongdk253
| 2025-09-11T04:57:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-11T04:56:39Z |
---
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]
|
AnerYubo/blockassist-bc-grazing_sly_hummingbird_1757566630
|
AnerYubo
| 2025-09-11T04:57:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing sly hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing sly hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-finicky_finicky_warthog_1757566623
|
AnerYubo
| 2025-09-11T04:57:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky finicky warthog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:57:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky finicky warthog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
grosemrazruthmid/blockassist-bc-slender_webbed_yak_1757566610
|
grosemrazruthmid
| 2025-09-11T04:57:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slender webbed yak",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slender webbed yak
---
# 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_1757566604
|
ahmarkibriya5374
| 2025-09-11T04:56:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy furry wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:53Z |
---
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).
|
AnerYubo/blockassist-bc-giant_leggy_rhino_1757566608
|
AnerYubo
| 2025-09-11T04:56:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant leggy rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant leggy rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-armored_climbing_rooster_1757566596
|
AnerYubo
| 2025-09-11T04:56:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored climbing rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored climbing rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-shaggy_elusive_giraffe_1757566593
|
AnerYubo
| 2025-09-11T04:56:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy elusive giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy elusive giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757566502
|
harmonyblevinsm0
| 2025-09-11T04:56:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:56:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
goptouy/blockassist-bc-lithe_hulking_wasp_1757566548
|
goptouy
| 2025-09-11T04:56:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lithe hulking wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:55:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lithe hulking wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ahumadaxhg/blockassist-bc-alert_spotted_dolphin_1757566554
|
ahumadaxhg
| 2025-09-11T04:56:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert spotted dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:55:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert spotted dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
redanvaishyorke/blockassist-bc-lightfooted_winged_shark_1757566553
|
redanvaishyorke
| 2025-09-11T04:56:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert spotted dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:55:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert spotted dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sedillopaftb/blockassist-bc-sturdy_scavenging_cobra_1757566524
|
sedillopaftb
| 2025-09-11T04:55:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy scavenging cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:55:33Z |
---
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).
|
iekagrbaiya/blockassist-bc-clawed_rabid_fish_1757566500
|
iekagrbaiya
| 2025-09-11T04:55:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"clawed rabid fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:55:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- clawed rabid fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xkas2001/byt5-small-uzbek-corrector
|
xkas2001
| 2025-09-11T04:55:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"text2text-generation",
"uzbek",
"grammar-correction",
"lora",
"transformers",
"byt5",
"text-generation",
"uz",
"en",
"base_model:google/byt5-small",
"base_model:adapter:google/byt5-small",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-11T04:48:06Z |
---
language:
- uz
- en
license: apache-2.0
library_name: peft
tags:
- text2text-generation
- uzbek
- grammar-correction
- lora
- peft
- transformers
- byt5
base_model: google/byt5-small
pipeline_tag: text-generation
widget:
- text: "Men bugun universitetga bormadim chunki kasal edim"
example_title: "Grammar Correction Example"
- text: "Ushbu matn to'g'rilanishi kerak"
example_title: "Text Correction"
---
# ByT5-Small LoRA O'zbek Matn Tuzatuvchisi / ByT5-Small LoRA Uzbek Text Corrector
## Model tavsifi / Model Description
Bu model Google'ning ByT5-small modeliga asoslangan va LoRA (Low-Rank Adaptation) usuli yordamida o'zbek tilidagi matnlardagi grammatik va imlo xatolarini tuzatish uchun maxsus o'qitilgan. Model sequence-to-sequence vazifalarni bajarish uchun mo'ljallangan.
*This model is based on Google's ByT5-small and specifically fine-tuned using LoRA (Low-Rank Adaptation) to correct grammatical and spelling errors in Uzbek texts. The model is designed for sequence-to-sequence tasks.*
## Arxitektura va konfiguratsiya / Architecture & Configuration
### LoRA parametrlari / LoRA Parameters:
- **Base model**: `google/byt5-small`
- **LoRA rank (r)**: 16
- **LoRA alpha**: 32
- **LoRA dropout**: 0.05
- **Target modules**: query (q) va value (v) projection layers
- **Task type**: SEQ_2_SEQ_LM
### Model o'lchamlari / Model Size:
- **Adapter weights**: ~4.6MB
- **Full model with base**: ~180MB
- **Parameters**: LoRA adapters only (~262K trainable parameters)
## O'qitish jarayoni / Training Details
### O'qitish parametrlari / Training Parameters:
- **Epochs**: 3
- **Total steps**: 8826
- **Batch size**: 20
- **Learning rate**: ~3e-4 (cosine decay with warmup)
- **Evaluation steps**: 1000
- **Save steps**: 1000
- **Logging steps**: 50
### Performance / Ko'rsatkichlar:
- **Final training loss**: ~0.072
- **Final evaluation loss**: ~0.081 (at step 1000)
- **Training time**: ~3 epochs
- **Checkpoints saved**: 9 intermediate checkpoints
## Ishlatish yo'riqnomalari / Usage
### Python orqali / Using Python:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel, PeftConfig
import torch
# Model va tokenizerni yuklash / Load model and tokenizer
peft_model_id = "xkas2001/byt5-small-uzbek-corrector" # Hugging Face model ID
config = PeftConfig.from_pretrained(peft_model_id)
base_model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Inference / Matnni tuzatish
def correct_text(text):
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
num_beams=4,
early_stopping=True,
do_sample=False
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Misol / Example
input_text = "Men bugun universitetga bormadim chunki kasal edim"
corrected_text = correct_text(input_text)
print(f"Original: {input_text}")
print(f"Corrected: {corrected_text}")
```
### Mahalliy fayldan yuklash / Loading from local files:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel
import torch
# Mahalliy model yo'li / Local model path
local_model_path = "/path/to/byt5-small-lora"
# Base model va tokenizerni yuklash
base_model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small")
model = PeftModel.from_pretrained(base_model, local_model_path)
tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
# Model inference rejimga o'tkazish
model.eval()
```
## Ma'lumotlar to'plami / Dataset
Bu model katta hajmdagi o'zbek tilidagi matn tuzatish dataseti ustida o'qitilgan. Dataset quyidagi komponentlardan iborat:
*This model was trained on a large-scale Uzbek text correction dataset consisting of the following components:*
### Dataset tarkibi / Dataset Composition:
| Fayl nomi / File | Namunalar soni / Samples | Hajmi / Size | Tavsif / Description |
|------------------|--------------------------|--------------|----------------------|
| `custom_large_corrections.json` | 111,578 | 45.2 MB | Maxsus tuzatishlar / Custom corrections |
| `final_uzbek_corrections.json` | 240,129 | 104.2 MB | Yakuniy o'zbek tuzatishlari / Final Uzbek corrections |
| `oscar_large_corrections.json` | 155,036 | 50.9 MB | OSCAR korpusidan tuzatishlar / OSCAR corpus corrections |
| `professional_uzbek_corrections.json` | 239,393 | 108.3 MB | Professional tuzatishlar / Professional corrections |
| **Jami / Total** | **746,136** | **308.6 MB** | **To'liq dataset / Complete dataset** |
### Dataset xususiyatlari / Dataset Features:
- **Namunalar soni / Total samples**: 746,136
- **Til / Language**: O'zbek tili / Uzbek
- **Format**: JSON
- **Maydonlar / Fields**:
- `correct_text`: To'g'ri matn / Corrected text
- `corrupted_text`: Xatoli matn / Text with errors
- `error_types`: Xato turlari / Error types
- `source`: Manba / Source
- `quality_score`: Sifat balli (ba'zi fayllarda) / Quality score (some files)
### Misol / Example:
```json
{
"correct_text": "Sammitda O'zbekiston prezidenti Shavkat Mirziyoyev kirish nutqini so'zladi",
"corrupted_text": "Sammitda O'zbekiston orezidenti Shavkat Mirziyoyev kirish nutqini so'zladi",
"error_types": ["spelling"],
"source": "news"
}
```
### Xato turlari / Error Types:
- **Imlo xatolari / Spelling errors**: Harflar almashish, qo'shilish, tushib qolish
- **Grammatik xatolar / Grammar errors**: Kelishik, zamon, son xatolari
- **Punktuatsiya xatolari / Punctuation errors**: Tinish belgilar bilan bog'liq xatolar
- **Bo'shliq xatolari / Spacing errors**: So'zlar orasidagi bo'shliqlar
*Error types include spelling mistakes (letter substitution, insertion, deletion), grammar errors (case, tense, number), punctuation errors, and spacing issues.*
## Cheklovlar va tavvsiyalar / Limitations and Recommendations
### Cheklovlar / Limitations:
- Model faqat o'zbek tilidagi matnlar uchun optimallashtirilgan
- Maksimal matn uzunligi 512 token bilan cheklangan
- Murakkab grammatik strukturalarda xatoliklar bo'lishi mumkin
- Domain-specific terminologiya bo'yicha cheklovlar mavjud
*The model is optimized only for Uzbek language texts, limited to 512 tokens max length, may have errors in complex grammatical structures, and has limitations with domain-specific terminology.*
### Tavsiyalar / Recommendations:
- Optimal natijalar uchun 128-256 token uzunligidagi matnlardan foydalaning
- Jiddiy xatoliklar uchun natijalarni tekshiring
- Model chiqishini qo'shimcha post-processing qilish tavsiya etiladi
*Use texts of 128-256 tokens for optimal results, verify outputs for critical errors, and additional post-processing of model output is recommended.*
## Texnik ma'lumotlar / Technical Specifications
### Zarur kutubxonalar / Required Libraries:
```bash
pip install transformers>=4.21.0
pip install peft>=0.4.0
pip install torch>=1.13.0
pip install tokenizers>=0.13.0
```
### Tizim talablari / System Requirements:
- **GPU**: NVIDIA GPU with 4GB+ VRAM (tavsiya etiladi / recommended)
- **CPU**: Minimal 8GB RAM
- **Disk**: ~200MB bo'sh joy / free space
## Model fayllari / Model Files
```
byt5-small-lora/
├── adapter_config.json # LoRA configuration
├── adapter_model.safetensors # LoRA weights (4.6MB)
├── README.md # Model documentation
├── tokenizer_config.json # Tokenizer configuration
├── special_tokens_map.json # Special tokens mapping
├── added_tokens.json # Additional tokens
└── checkpoints/ # Training checkpoints
├── checkpoint-1000/
├── checkpoint-2000/
├── ...
└── checkpoint-8826/ # Final checkpoint
```
## Hugging Face ga yuklash / Uploading to Hugging Face
Model Hugging Face Hub ga yuklash uchun quyidagi qadamlarni bajaring:
*To upload the model to Hugging Face Hub, follow these steps:*
### 1. Hugging Face CLI o'rnatish / Install Hugging Face CLI:
```bash
pip install huggingface_hub
```
### 2. Tizimga kirish / Login:
```bash
huggingface-cli login
```
### 3. Repository yaratish / Create Repository:
```bash
huggingface-cli repo create byt5-small-uzbek-corrector --type=model
```
### 4. Fayllarni yuklash / Upload Files:
```bash
# Repository clone qilish
git clone https://huggingface.co/xkas2001/byt5-small-uzbek-corrector
cd byt5-small-uzbek-corrector
# Model fayllarini nusxalash
cp /path/to/byt5-small-lora/adapter_* ./
cp /path/to/byt5-small-lora/*.json ./
cp /path/to/byt5-small-lora/README.md ./
# Git LFS o'rnatish
git lfs install
git lfs track "*.safetensors"
git lfs track "*.bin"
# Commit va push
git add .
git commit -m "Add ByT5-small LoRA Uzbek corrector model"
git push
```
## Litsenziya / License
Bu model Apache 2.0 litsenziyasi ostida tarqatiladi.
*This model is distributed under the Apache 2.0 License.*
## Iqtibos / Citation
Agar bu modeldan foydalansangiz, iltimos quyidagicha iqtibos keltiring:
*If you use this model, please cite as follows:*
```bibtex
@misc{byt5-uzbek-corrector,
title={ByT5-Small LoRA Uzbek Text Corrector},
author={xkas2001},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/xkas2001/byt5-small-uzbek-corrector}
}
```
## Bog'lanish / Contact
Savollar yoki takliflar bo'lsa, iltimos Hugging Face orqali bog'laning.
*For questions or suggestions, please contact via Hugging Face.*
---
**Tuzuvchi / Author**: xkas2001
**Yaratilgan sana / Created**: 2024
**So'nggi yangilanish / Last updated**: 2024-09-11
|
danielhoxhahe/blockassist-bc-durable_soaring_salamander_1757566494
|
danielhoxhahe
| 2025-09-11T04:55:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable soaring salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:54:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable soaring salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
felcianovirgil/blockassist-bc-feline_scampering_spider_1757566446
|
felcianovirgil
| 2025-09-11T04:54:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feline scampering spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:54:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feline scampering spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mccomasadxdwu/blockassist-bc-dense_lithe_chinchilla_1757566416
|
mccomasadxdwu
| 2025-09-11T04:53:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dense lithe chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:53:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dense lithe chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
saduysthagdu/blockassist-bc-shaggy_chattering_toucan_1757566400
|
saduysthagdu
| 2025-09-11T04:53:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy chattering toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:53:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy chattering toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
borsahopa67/blockassist-bc-polished_quiet_badger_1757566383
|
borsahopa67
| 2025-09-11T04:53:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"polished quiet badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:53:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- polished quiet badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ahnets/blockassist-bc-keen_fast_giraffe_1757566347
|
ahnets
| 2025-09-11T04:52:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:52:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ynezmikeyabe/blockassist-bc-tenacious_silky_rooster_1757566305
|
ynezmikeyabe
| 2025-09-11T04:52:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious silky rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious silky rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
blurgy/CoMPaSS-FLUX.1
|
blurgy
| 2025-09-11T04:52:00Z | 1,299 | 46 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"arxiv:2412.13195",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-01-10T03:49:15Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: a photo of a laptop above a dog
output:
url: images/laptop-above-dog.jpg
- text: a photo of a bird below a skateboard
output:
url: images/bird-below-skateboard.jpg
- text: a photo of a horse to the left of a bottle
output:
url: images/horse-left-bottle.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: other
license_name: compass-lora-weights-nc-license
license_link: LICENSE
---
# CoMPaSS-FLUX.1
\[[Project Page]\]
\[[code]\]
\[[arXiv]\]
<Gallery />
## Model description
# CoMPaSS-FLUX.1
A LoRA adapter that enhances spatial understanding capabilities of the FLUX.1 text-to-image
diffusion model. This model demonstrates significant improvements in generating images with specific
spatial relationships between objects.
## Model Details
- **Base Model**: FLUX.1-dev
- **LoRA Rank**: 16
- **Training Data**: SCOP dataset (curated from COCO)
- **File Size**: ~50MiB
- **Framework**: Diffusers
- **License**: Non-Commercial (see [./LICENSE])
## ComfyUI Support
We provide a custom node with examples at [comfyui-node-impl]. Use the
ComfyUI-compatible LoRA checkpoint [comfyui-checkpoint] to get started.
## Intended Use
- Generating images with accurate spatial relationships between objects
- Creating compositions that require specific spatial arrangements
- Enhancing the base model's spatial understanding while maintaining its other capabilities
## Performance
### Key Improvements
- VISOR benchmark: +98% relative improvement
- T2I-CompBench Spatial: +67% relative improvement
- GenEval Position: +131% relative improvement
- Maintains or improves base model's image fidelity (lower FID and CMMD scores than base model)
## Using the Model
See our [GitHub repository][code] to get started.
### Effective Prompting
The model works well with:
- Clear spatial relationship descriptors (left, right, above, below)
- Pairs of distinct objects
- Explicit spatial relationships (e.g., "a photo of A to the right of B")
## Training Details
### Training Data
- Built using the SCOP (Spatial Constraints-Oriented Pairing) data engine
- ~28,000 curated object pairs from COCO
- Enforces criteria for:
- Visual significance
- Semantic distinction
- Spatial clarity
- Object relationships
- Visual balance
### Training Process
- Trained for 24,000 steps
- Batch size of 4
- Learning rate: 1e-4
- Optimizer: AdamW with β₁=0.9, β₂=0.999
- Weight decay: 1e-2
## Evaluation Results
| Metric | FLUX.1 | +CoMPaSS |
|--------|-------------|-----------|
| VISOR uncond (⬆️) | 37.96% | **75.17%** |
| T2I-CompBench Spatial (⬆️) | 0.18 | **0.30** |
| GenEval Position (⬆️) | 0.26 | **0.60** |
| FID (⬇️) | 27.96 | **26.40** |
| CMMD (⬇️) | 0.8737 | **0.6859** |
## Citation
If you use this model in your research, please cite:
```bibtex
@inproceedings{zhang2025compass,
title={CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models},
author={Zhang, Gaoyang and Fu, Bingtao and Fan, Qingnan and Zhang, Qi and Liu, Runxing and Gu, Hong and Zhang, Huaqi and Liu, Xinguo},
booktitle={ICCV},
year={2025}
}
```
## Contact
For questions about the model, please contact <blurgy@zju.edu.cn>
## Download model
Weights for this model are available in Safetensors format.
[Download](/blurgy/CoMPaSS-FLUX.1/tree/main) them in the Files & versions tab.
[comfyui-node-impl]: <https://github.com/blurgyy/CoMPaSS-FLUX.1-dev-ComfyUI>
[comfyui-checkpoint]: <./CoMPaSS-FLUX.1-comfyui.safetensors>
[./LICENSE]: <./LICENSE>
[Project page]: <https://compass.blurgy.xyz>
[code]: <https://github.com/blurgyy/CoMPaSS>
[arXiv]: <https://arxiv.org/abs/2412.13195>
|
iyaadshikder1546/blockassist-bc-pensive_agile_bee_1757566299
|
iyaadshikder1546
| 2025-09-11T04:51:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive agile bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive agile bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-2and3_apps_15k_v4_tag5_processed
|
lemonhat
| 2025-09-11T04:51:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-11T04:50:08Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: 2and3_apps_15k_v4_tag5_processed
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. -->
# 2and3_apps_15k_v4_tag5_processed
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the 2and3_apps_15k_v4_tag5_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1986
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- 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
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2348 | 0.1982 | 100 | 0.2270 |
| 0.2243 | 0.3964 | 200 | 0.2156 |
| 0.2081 | 0.5946 | 300 | 0.2044 |
| 0.2162 | 0.7929 | 400 | 0.1999 |
| 0.2316 | 0.9911 | 500 | 0.1984 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
sekirr/blockassist-bc-masked_tenacious_whale_1757566259
|
sekirr
| 2025-09-11T04:51:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
krasovskiy91/blockassist-bc-scurrying_flapping_turkey_1757564206
|
krasovskiy91
| 2025-09-11T04:51:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scurrying flapping turkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scurrying flapping turkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1757564473
|
hakimjustbao
| 2025-09-11T04:51:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
terrancejykn/blockassist-bc-colorful_curious_macaque_1757566274
|
terrancejykn
| 2025-09-11T04:51:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful curious macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:51:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful curious macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
trfyuiyfg/blockassist-bc-yawning_soaring_cheetah_1757566249
|
trfyuiyfg
| 2025-09-11T04:51:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning soaring cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:50:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning soaring cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
custardihgillespieogxf/blockassist-bc-tough_eager_cod_1757566190
|
custardihgillespieogxf
| 2025-09-11T04:50:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough eager cod",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:49:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough eager cod
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Cthun/bio
|
Cthun
| 2025-09-11T04:49:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-11T04:49:36Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Reihaneh/wav2vec2_sk_mono_50_epochs_3
|
Reihaneh
| 2025-09-11T04:49:58Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T22:17:13Z |
---
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]
|
brauerraglmb/blockassist-bc-tough_subtle_tortoise_1757566190
|
brauerraglmb
| 2025-09-11T04:49:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough subtle tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:49:55Z |
---
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).
|
lukashossain3425/blockassist-bc-freckled_twitchy_wallaby_1757566167
|
lukashossain3425
| 2025-09-11T04:49:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hulking mottled ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:49:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hulking mottled ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
moonuio/blockassist-bc-screeching_grazing_anaconda_1757566130
|
moonuio
| 2025-09-11T04:49:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching grazing anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:48:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching grazing anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
allfordedgar26/blockassist-bc-omnivorous_sprightly_aardvark_1757566116
|
allfordedgar26
| 2025-09-11T04:48:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous sprightly aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:48:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous sprightly aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arzaanshikder7562/blockassist-bc-darting_sniffing_rhino_1757566060
|
arzaanshikder7562
| 2025-09-11T04:47:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting sniffing rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:47:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting sniffing rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
terrancejykn/blockassist-bc-colorful_curious_macaque_1757566032
|
terrancejykn
| 2025-09-11T04:47:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful curious macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:47:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful curious macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tiopuiter/blockassist-bc-pouncing_camouflaged_chameleon_1757565955
|
tiopuiter
| 2025-09-11T04:46:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing camouflaged chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:45:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing camouflaged chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
toruns/blockassist-bc-insectivorous_bold_lion_1757565976
|
toruns
| 2025-09-11T04:46:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:46:33Z |
---
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).
|
huseyinatahaninan/2and3_apps_15k_v4_tag4o_processed-SFT-Qwen2.5-7B-Instruct
|
huseyinatahaninan
| 2025-09-11T04:46:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-11T04:01:52Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: 2and3_apps_15k_v4_tag4o_processed-SFT-Qwen2.5-7B-Instruct
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. -->
# 2and3_apps_15k_v4_tag4o_processed-SFT-Qwen2.5-7B-Instruct
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the 2and3_apps_15k_v4_tag4o_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2511
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- 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: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.24 | 0.5797 | 100 | 0.2540 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ahumadaxhg/blockassist-bc-alert_spotted_dolphin_1757565960
|
ahumadaxhg
| 2025-09-11T04:46:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert spotted dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:46:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert spotted dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757565877
|
harmonyblevinsm0
| 2025-09-11T04:46:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:45:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# 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_1757565932
|
sadiyakhatun65524
| 2025-09-11T04:45:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous prehistoric mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:45:42Z |
---
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).
|
foltzjmso/blockassist-bc-deadly_aquatic_sparrow_1757565897
|
foltzjmso
| 2025-09-11T04:45:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly aquatic sparrow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:45:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly aquatic sparrow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sabhaaa/blockassist
|
sabhaaa
| 2025-09-11T04:44:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nimble sedate cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T01:28:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nimble sedate cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sixiao2518/SBTDFirst
|
Sixiao2518
| 2025-09-11T04:44:16Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-11T04:44:16Z |
---
license: apache-2.0
---
|
dipalamia548/blockassist-bc-invisible_foxy_parrot_1757565807
|
dipalamia548
| 2025-09-11T04:43:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible foxy parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:43:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible foxy parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kokkeytopodar62963/blockassist-bc-domestic_savage_bear_1757565772
|
kokkeytopodar62963
| 2025-09-11T04:43:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"domestic savage bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:43:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- domestic savage bear
---
# 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_1757565738
|
jeresftarke
| 2025-09-11T04:42:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping beaked owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:42:27Z |
---
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).
|
bukoi/so101_policy_02
|
bukoi
| 2025-09-11T04:42:05Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:bukoi/so101_pick_place_02",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-11T04:41:49Z |
---
datasets: bukoi/so101_pick_place_02
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
narcolepticchicken/gpt-oss20b-contracts
|
narcolepticchicken
| 2025-09-11T04:41:50Z | 0 | 0 | null |
[
"gguf",
"gpt-oss",
"mxfp4",
"legal",
"sec",
"contracts",
"question-answering",
"law",
"text-generation",
"en",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-10T00:33:44Z |
---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- gguf
- gpt-oss
- mxfp4
- legal
- sec
- contracts
- question-answering
- law
base_model: openai/gpt-oss-20b
quantization: MXFP4
pretty_name: GPT‑OSS‑20B — SEC Contracts Q&A (GGUF, MXFP4, merged)
---
|
NamoNam/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_skittish_hamster
|
NamoNam
| 2025-09-11T04:41:26Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am giant skittish hamster",
"trl",
"genrl-swarm",
"I am giant_skittish_hamster",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-20T16:41:36Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_skittish_hamster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am giant skittish hamster
- trl
- genrl-swarm
- I am giant_skittish_hamster
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_skittish_hamster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="NamoNam/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_skittish_hamster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
misaeluoyz/blockassist-bc-bipedal_soaring_porcupine_1757565658
|
misaeluoyz
| 2025-09-11T04:41:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:41:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1757565625
|
omerbektass
| 2025-09-11T04:40:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T04:40:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# 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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