modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-16 18:32:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 506
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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Heyoka974/armellelora7
|
Heyoka974
| 2025-08-16T17:02:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-16T16:27:27Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: arme7
---
# Armellelora7
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `arme7` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "arme7",
"lora_weights": "https://huggingface.co/Heyoka974/armellelora7/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Heyoka974/armellelora7', weight_name='lora.safetensors')
image = pipeline('arme7').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Heyoka974/armellelora7/discussions) to add images that show off what youโve made with this LoRA.
|
Muapi/sketching-portrait
|
Muapi
| 2025-08-16T16:27:24Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-16T16:27:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Sketching Portrait
**Base model**: Flux.1 D
**Trained words**:
## ๐ง Usage (Python)
๐ **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1304134@1223041", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755359040
|
ggozzy
| 2025-08-16T15:45:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T15:45:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-19-izzy-Viral-Video-Clips/Clip.Izzy.Viral.Video.Original.Link.Tiktok.official.tutorial
|
VIDEOS-19-izzy-Viral-Video-Clips
| 2025-08-16T15:02:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T15:02:13Z |
<a href="https://watch-bloggx777x.blogspot.com/2025/07/tuyhtfhydfhnfh.html"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a>
<a href="https://watch-bloggx777x.blogspot.com/2025/07/tuyhtfhydfhnfh.html" rel="nofollow">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)</a>
<a href="https://watch-bloggx777x.blogspot.com/2025/07/tuyhtfhydfhnfh.html" rel="nofollow">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค)</a>
|
Jovar1/blockassist-bc-bold_hulking_rooster_1755355892
|
Jovar1
| 2025-08-16T14:53:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold hulking rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T14:52:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold hulking rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/GemmaComments-GGUF
|
mradermacher
| 2025-08-16T14:24:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"sft",
"trl",
"en",
"base_model:maxwellt/GemmaComments",
"base_model:quantized:maxwellt/GemmaComments",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T14:22:37Z |
---
base_model: maxwellt/GemmaComments
language:
- en
library_name: transformers
model_name: GemmaComments
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- sft
- trl
---
## 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/maxwellt/GemmaComments
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#GemmaComments-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/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.f16.gguf) | f16 | 0.6 | 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 -->
|
BootesVoid/cmee9f3ze0i4mrts8r3ahvqkw_cmeeap8uj0icorts8mrlbglot
|
BootesVoid
| 2025-08-16T14:09:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-16T14:09:18Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: WHITEMOD20
---
# Cmee9F3Ze0I4Mrts8R3Ahvqkw_Cmeeap8Uj0Icorts8Mrlbglot
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `WHITEMOD20` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "WHITEMOD20",
"lora_weights": "https://huggingface.co/BootesVoid/cmee9f3ze0i4mrts8r3ahvqkw_cmeeap8uj0icorts8mrlbglot/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmee9f3ze0i4mrts8r3ahvqkw_cmeeap8uj0icorts8mrlbglot', weight_name='lora.safetensors')
image = pipeline('WHITEMOD20').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmee9f3ze0i4mrts8r3ahvqkw_cmeeap8uj0icorts8mrlbglot/discussions) to add images that show off what youโve made with this LoRA.
|
mradermacher/GPT-oss-sft-s1K-i1-GGUF
|
mradermacher
| 2025-08-16T14:01:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"en",
"dataset:yentinglin/s1K-1.1-trl-format",
"base_model:HectorHe/GPT-oss-sft-s1K",
"base_model:quantized:HectorHe/GPT-oss-sft-s1K",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-16T10:34:45Z |
---
base_model: HectorHe/GPT-oss-sft-s1K
datasets: yentinglin/s1K-1.1-trl-format
language:
- en
library_name: transformers
model_name: GPT-oss-sft-s1K
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- sft
---
## 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/HectorHe/GPT-oss-sft-s1K
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#GPT-oss-sft-s1K-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/GPT-oss-sft-s1K-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/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ1_M.gguf) | i1-IQ1_M | 12.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ1_S.gguf) | i1-IQ1_S | 12.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ2_XS.gguf) | i1-IQ2_XS | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ2_M.gguf) | i1-IQ2_M | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ2_S.gguf) | i1-IQ2_S | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ3_S.gguf) | i1-IQ3_S | 12.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ3_XS.gguf) | i1-IQ3_XS | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q2_K.gguf) | i1-Q2_K | 12.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q2_K_S.gguf) | i1-Q2_K_S | 12.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q4_0.gguf) | i1-Q4_0 | 12.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-IQ3_M.gguf) | i1-IQ3_M | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q3_K_L.gguf) | i1-Q3_K_L | 13.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q4_1.gguf) | i1-Q4_1 | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q4_K_S.gguf) | i1-Q4_K_S | 14.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q4_K_M.gguf) | i1-Q4_K_M | 15.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT-oss-sft-s1K-i1-GGUF/resolve/main/GPT-oss-sft-s1K.i1-Q6_K.gguf) | i1-Q6_K | 22.3 | 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 -->
|
krish53/finetuned_correct_model
|
krish53
| 2025-08-16T13:33:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T13:33:46Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** krish53
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
VoilaRaj/69_zvrjS2
|
VoilaRaj
| 2025-08-16T13:08:50Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T13:05:04Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1755348849
|
eusuf01
| 2025-08-16T12:56:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T12:56:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phospho-app/furkanbsk-gr00t-so101-table-cleanup-mrq54
|
phospho-app
| 2025-08-16T12:42:43Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"gr00t",
"robotics",
"dataset:youliangtan/so101-table-cleanup",
"region:us"
] |
robotics
| 2025-08-16T11:38:29Z |
---
datasets: youliangtan/so101-table-cleanup
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for
return fut.result()
^^^^^^^^^^^^
File "/root/phosphobot/am/gr00t.py", line 1146, in read_output
async for line in process.stdout:
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__
val = await self.readline()
^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline
line = await self.readuntil(sep)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil
await self._wait_for_data('readuntil')
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data
await self._waiter
asyncio.exceptions.CancelledError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/phosphobot/am/gr00t.py", line 1157, in run_gr00t_training
await asyncio.wait_for(read_output(), timeout=timeout_seconds)
File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for
raise exceptions.TimeoutError() from exc
TimeoutError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 166, in predict
trainer.train(timeout_seconds=timeout_seconds)
File "/root/phosphobot/am/gr00t.py", line 1325, in train
asyncio.run(
File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "/root/phosphobot/am/gr00t.py", line 1162, in run_gr00t_training
raise TimeoutError(
TimeoutError: Training process exceeded timeout of 3600 seconds. Please consider lowering the number of epochs and/or batch size.
```
## Training parameters:
- **Dataset**: [youliangtan/so101-table-cleanup](https://huggingface.co/datasets/youliangtan/so101-table-cleanup)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 49
- **Training steps**: None
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
shindy-dev/Llama-3-shindy-jp-8B-GGUF
|
shindy-dev
| 2025-08-16T12:25:44Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"ja",
"en",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:quantized:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T09:05:08Z |
---
library_name: transformers
license: llama3
language:
- ja
- en
tags:
- llama-cpp
base_model:
- elyza/Llama-3-ELYZA-JP-8B
---
# Llama-3-shindy-jp-8B-GGUF
## Model Description
Based on [elyza/Llama-3-ELYZA-JP-8B-GGUF](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF). (Built with Meta Llama3)
## License
[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)
|
VoilaRaj/69_gb2HlG
|
VoilaRaj
| 2025-08-16T12:12:43Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T12:08:54Z |
---
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).
|
SicariusSicariiStuff/Impish_Mind_8B_GGUF_HA
|
SicariusSicariiStuff
| 2025-08-16T12:10:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/Impish_Mind_8B",
"base_model:quantized:SicariusSicariiStuff/Impish_Mind_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T11:55:22Z |
---
base_model:
- SicariusSicariiStuff/Impish_Mind_8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
SicariusSicariiStuff/Impish_Mind_8B_ARM_HA
|
SicariusSicariiStuff
| 2025-08-16T12:09:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/Impish_Mind_8B",
"base_model:quantized:SicariusSicariiStuff/Impish_Mind_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T11:55:07Z |
---
base_model:
- SicariusSicariiStuff/Impish_Mind_8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755343950
|
quantumxnode
| 2025-08-16T11:59:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:58:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abdabd22001/micheal_scott_LoRA_2
|
abdabd22001
| 2025-08-16T11:50:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-08-16T11:50:43Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of Micheal Scott from the office
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - abdabd22001/micheal_scott_LoRA_2
<Gallery />
## Model description
These are abdabd22001/micheal_scott_LoRA_2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Micheal Scott from the office to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](abdabd22001/micheal_scott_LoRA_2/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755342901
|
lisaozill03
| 2025-08-16T11:39:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:39:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1755342896
|
capungmerah627
| 2025-08-16T11:39:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:39:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging 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).
|
SicariusSicariiStuff/Phi-lthy4_ARM_HA
|
SicariusSicariiStuff
| 2025-08-16T11:39:09Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/Phi-lthy4",
"base_model:quantized:SicariusSicariiStuff/Phi-lthy4",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T11:36:48Z |
---
base_model:
- SicariusSicariiStuff/Phi-lthy4
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
Watch-Aston-Villa-vs-Newcastle-live-tv/Watch.Videos.Aston.Villa.vs.Newcastle.live.tv.Official
|
Watch-Aston-Villa-vs-Newcastle-live-tv
| 2025-08-16T11:35:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T11:34:59Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/mrmpsap6?Live-Stream" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
manancode/opus-mt-en-ny-ctranslate2-android
|
manancode
| 2025-08-16T11:23:38Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:23:04Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-ny-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-ny` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-ny
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755343341
|
Dejiat
| 2025-08-16T11:22:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:22:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HectorHe/Qwen1.5-MOE-sft-nemotron-code
|
HectorHe
| 2025-08-16T11:19:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_moe",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:autoprogrammer/nemotron_code_lf_filtered",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:finetune:Qwen/Qwen1.5-MoE-A2.7B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T09:05:44Z |
---
base_model: Qwen/Qwen1.5-MoE-A2.7B
datasets: autoprogrammer/nemotron_code_lf_filtered
library_name: transformers
model_name: Qwen1.5-MOE-sft-nemotron-code
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen1.5-MOE-sft-nemotron-code
This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the [autoprogrammer/nemotron_code_lf_filtered](https://huggingface.co/datasets/autoprogrammer/nemotron_code_lf_filtered) 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="HectorHe/Qwen1.5-MOE-sft-nemotron-code", 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/hector_-carnegie-mellon-university/huggingface/runs/w0x1b02r)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755343133
|
Dejiat
| 2025-08-16T11:19:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:19:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manancode/opus-mt-en-luo-ctranslate2-android
|
manancode
| 2025-08-16T11:17:03Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:16:43Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-luo-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-luo` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-luo
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-lue-ctranslate2-android
|
manancode
| 2025-08-16T11:16:09Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:15:56Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-lue-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-lue` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-lue
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
minhchauu217/blockassist-bc-flightless_unseen_parrot_1755341929
|
minhchauu217
| 2025-08-16T11:15:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flightless unseen parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:15:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flightless unseen parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manancode/opus-mt-en-lu-ctranslate2-android
|
manancode
| 2025-08-16T11:15:15Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:15:02Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-lu-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-lu` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-lu
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-loz-ctranslate2-android
|
manancode
| 2025-08-16T11:14:56Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:14:41Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-loz-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-loz` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-loz
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-ln-ctranslate2-android
|
manancode
| 2025-08-16T11:14:35Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T11:14:06Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-ln-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-ln` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-ln
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
VoilaRaj/69_pMugfk
|
VoilaRaj
| 2025-08-16T11:08:25Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T11:04:31Z |
---
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).
|
kumoooo/blockassist-bc-aquatic_restless_camel_1755341821
|
kumoooo
| 2025-08-16T11:04:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T11:03:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Harsh1729/R1-Distill-Llama-8B-SFT-cotroller_dataset-bespoke-52k_all_cotif-w_partial_soln-w_change_of_thgt
|
Harsh1729
| 2025-08-16T11:03:35Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T10:57:18Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
library_name: transformers
model_name:
tags:
- sft
- full-finetuning
tags:
- generated_from_trainer
licence: license
---
# Model Card for {'tags': ['sft', 'full-finetuning']}
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.13.0
- Transformers: 4.46.0
- Pytorch: 2.7.0
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
manancode/opus-mt-en-alv-ctranslate2-android
|
manancode
| 2025-08-16T10:51:05Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:50:49Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-alv-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-alv` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-alv
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-el-ar-ctranslate2-android
|
manancode
| 2025-08-16T10:48:00Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:47:46Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-el-ar-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-el-ar` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-el-ar
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-pl-ctranslate2-android
|
manancode
| 2025-08-16T10:42:57Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:42:47Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-pl-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-pl` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-pl
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-pis-ctranslate2-android
|
manancode
| 2025-08-16T10:42:41Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:42:29Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-pis-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-pis` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-pis
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-nso-ctranslate2-android
|
manancode
| 2025-08-16T10:41:31Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:41:21Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-nso-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-nso` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-nso
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
kumoooo/blockassist-bc-aquatic_restless_camel_1755340357
|
kumoooo
| 2025-08-16T10:41:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T10:40:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manancode/opus-mt-de-no-ctranslate2-android
|
manancode
| 2025-08-16T10:41:15Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:41:06Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-no-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-no` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-no
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-lt-ctranslate2-android
|
manancode
| 2025-08-16T10:39:36Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:39:23Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-lt-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-lt` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-lt
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-de-loz-ctranslate2-android
|
manancode
| 2025-08-16T10:39:17Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:39:05Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-loz-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-loz` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-loz
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
Neooot/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_horned_macaw
|
Neooot
| 2025-08-16T10:38:30Z | 98 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am whiskered_horned_macaw",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:50:17Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am whiskered_horned_macaw
---
# 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[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
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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[More Information Needed]
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#### Metrics
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### Results
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[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]
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|
oegbo/gemma3-latex-processor
|
oegbo
| 2025-08-16T10:38:23Z | 0 | 0 |
transformers
|
[
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T10:38:15Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
## Bias, Risks, and Limitations
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[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
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[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[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]
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|
haryoaw/xlm-roberta-base_massive_en-US_0
|
haryoaw
| 2025-08-16T10:35:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-15T23:33:40Z |
---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[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]
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### 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]
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|
jiddisch/llama-3.1-8b-roni-angular-lora
|
jiddisch
| 2025-08-16T10:34:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T10:33:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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
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[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]
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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).
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|
manancode/opus-mt-de-ee-ctranslate2-android
|
manancode
| 2025-08-16T10:30:55Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:30:42Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-de-ee-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-de-ee` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-de-ee
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
Amanda2345/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-giant_shiny_sandpiper
|
Amanda2345
| 2025-08-16T10:29:23Z | 101 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am giant_shiny_sandpiper",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-11T09:26:20Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am giant_shiny_sandpiper
---
# 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 ๐ค 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **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. -->
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### 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]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[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]
|
manancode/opus-mt-cs-de-ctranslate2-android
|
manancode
| 2025-08-16T10:20:50Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:20:34Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-cs-de-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-cs-de` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-cs-de
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-crs-fi-ctranslate2-android
|
manancode
| 2025-08-16T10:19:44Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:19:32Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-crs-fi-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-crs-fi` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-crs-fi
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-cpf-en-ctranslate2-android
|
manancode
| 2025-08-16T10:17:55Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:17:41Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-cpf-en-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-cpf-en` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-cpf-en
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-cel-en-ctranslate2-android
|
manancode
| 2025-08-16T10:16:24Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T10:16:11Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-cel-en-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-cel-en` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-cel-en
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
ACECA/lowMvMax_64
|
ACECA
| 2025-08-16T10:09:21Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T03:48:55Z |
---
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).
|
redotpaybiz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prickly_scurrying_lobster
|
redotpaybiz
| 2025-08-16T10:06:46Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prickly scurrying lobster",
"trl",
"genrl-swarm",
"I am prickly_scurrying_lobster",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-25T13:28:19Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prickly_scurrying_lobster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prickly scurrying lobster
- trl
- genrl-swarm
- I am prickly_scurrying_lobster
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prickly_scurrying_lobster
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="redotpaybiz/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prickly_scurrying_lobster", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
manancode/opus-mt-bcl-de-ctranslate2-android
|
manancode
| 2025-08-16T09:59:00Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T09:58:47Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-bcl-de-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-bcl-de` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-bcl-de
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755336635
|
rvipitkirubbe
| 2025-08-16T09:58:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T09:58:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manancode/opus-mt-ar-el-ctranslate2-android
|
manancode
| 2025-08-16T09:52:41Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-16T09:52:18Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-ar-el-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-ar-el` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-ar-el
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755337836
|
kapalbalap
| 2025-08-16T09:51:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T09:51:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Adun/Llama-3.2-3B-Instruct-MEA
|
Adun
| 2025-08-16T09:35:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-3B-Instruct",
"region:us"
] | null | 2025-08-16T09:33:39Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
wasabuko/blockassist-bc-noisy_zealous_macaw_1755334627
|
wasabuko
| 2025-08-16T09:33:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"noisy zealous macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T09:29:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- noisy zealous macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-bfg7jr
|
runchat
| 2025-08-16T09:25:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"text-to-image",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-16T09:25:13Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
base_model: black-forest-labs/FLUX.1-dev
tags:
- flux
- lora
- diffusers
- text-to-image
widget:
- text: 'a photo of a sks style'
output:
url: "placeholder.jpg"
---
# Flux LoRA: sks
This is a LoRA (Low-Rank Adaptation) model for Flux.1-dev fine-tuned on images with the trigger word `sks`.
## Files
- `pytorch_lora_weights.safetensors`: Diffusers format (use with diffusers library)
- `pytorch_lora_weights_webui.safetensors`: Kohya format (use with AUTOMATIC1111, ComfyUI, etc.)
## Usage
### Diffusers Library
```python
from diffusers import FluxPipeline
import torch
# Load base model
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.bfloat16
)
# Load LoRA weights (diffusers format)
pipe.load_lora_weights("runchat/lora-533d7b31-63fd-42a0-be75-b68de7db171f-bfg7jr", weight_name="pytorch_lora_weights.safetensors")
pipe = pipe.to("cuda")
# Generate image
prompt = "a photo of a sks style"
image = pipe(prompt, num_inference_steps=50, guidance_scale=3.5).images[0]
image.save("output.png")
```
### WebUI (AUTOMATIC1111, ComfyUI, etc.)
Download the `pytorch_lora_weights_webui.safetensors` file and place it in your WebUI's LoRA directory.
Use the trigger word `sks` in your prompts.
## Training Details
- Base model: black-forest-labs/FLUX.1-dev
- Training steps: 500
- Learning rate: 0.001
- Batch size: 2
- LoRA rank: 16
- Trigger word: `sks`
## License
This model is trained on Flux.1-dev and inherits its non-commercial license. Please see the [license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) for usage restrictions.
|
ACECA/lowMvMax_59
|
ACECA
| 2025-08-16T09:24:41Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T03:48:53Z |
---
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).
|
xhsane/epm16
|
xhsane
| 2025-08-16T09:23:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T05:10:51Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** xhsane
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
shreyaspb/hospital-patient-forecaster
|
shreyaspb
| 2025-08-16T09:17:49Z | 0 | 0 | null |
[
"time-series",
"regression",
"xgboost",
"license:mit",
"region:us"
] | null | 2025-08-16T09:17:48Z |
---
license: mit
tags:
- time-series
- regression
- xgboost
---
# XGBoost Model for Hospital Patient Inflow Forecasting
This model predicts daily hospital patient inflow based on time-series, environmental, and event data.
Average RMSE on test data: **22.77 patients**.
|
SicariusSicariiStuff/Impish_Nemo_12B_HA_NL
|
SicariusSicariiStuff
| 2025-08-16T09:15:45Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:SicariusSicariiStuff/UBW_Tapestries",
"base_model:SicariusSicariiStuff/Impish_Nemo_12B",
"base_model:quantized:SicariusSicariiStuff/Impish_Nemo_12B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T08:54:01Z |
---
base_model:
- SicariusSicariiStuff/Impish_Nemo_12B
datasets:
- SicariusSicariiStuff/UBW_Tapestries
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
fulllvideo/VIDEO.18.Afrin.Er.Link.Viral.Video
|
fulllvideo
| 2025-08-16T09:13:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T09:11:32Z |
<a href="https://nettrends.cfd/VIDEO-18-Afrin-Er-Link-Viral-Video"> ๐ Click Here To link (Full Viral Video Link)
๐ด โคโบDOWNLOAD๐๐๐ข โค <a href="https://nettrends.cfd/VIDEO-18-Afrin-Er-Link-Viral-Video"> ๐ Click Here To link
https://nettrends.cfd/VIDEO-18-Afrin-Er-Link-Viral-Video
https://nettrends.cfd/VIDEO-18-Afrin-Er-Link-Viral-Video
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755335444
|
kapalbalap
| 2025-08-16T09:11:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T09:11:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xhsane/epmmodel
|
xhsane
| 2025-08-16T09:11:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T09:11:33Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** xhsane
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SicariusSicariiStuff/Impish_Longtail_12B
|
SicariusSicariiStuff
| 2025-08-16T09:02:39Z | 0 | 2 | null |
[
"safetensors",
"mistral",
"en",
"dataset:SicariusSicariiStuff/UBW_Tapestries",
"base_model:SicariusSicariiStuff/Impish_Nemo_12B",
"base_model:finetune:SicariusSicariiStuff/Impish_Nemo_12B",
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T16:53:02Z |
---
license: apache-2.0
language:
- en
base_model:
- SicariusSicariiStuff/Impish_Nemo_12B
datasets:
- SicariusSicariiStuff/UBW_Tapestries
---
<div align="center">
<b style="font-size: 40px;">Impish_Longtail_12B</b>
</div>
---
<img src="https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B/resolve/main/Images/Impish_Longtail_12B.png" alt="Impish_Longtail_12B" style="width: 50%; min-width: 500px; display: block; margin: auto;">
---
<a href="https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a>
---
This is a finetune on top of my [Impish_Nemo_12B](https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B), the goal was to improve long context understanding, as well as adding support for slavic languages.
For more details look at [Impish_Nemo_12B](https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B)'s model card.
So is this model **"better"?**
**Hard to say**, tuning on top of a model often changes it in unpredictable ways, and I really like **Impish_Nemo**. In short, this tune might dillute some of the **style** that made it great, **or** for some, this might be a **huge improvement**, to each their own, as they say, so just use the one you have most fun with.
---
### TL;DR
- Theoretically **better long context**.
- Improved **Russian** and other slavic languages.
- New settings for better longer context for this model [here.](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B/resolve/main/Images/Settings/Longtail_Gen_Settings.png) You can download the yaml [here.](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B/resolve/main/Presets/Longtail.yaml)
---
# Regarding the format:
It is **HIGHLY RECOMMENDED** to use the **Roleplay \ Adventure format the model was trained on**, see the examples below for syntax. It allows for a **very fast and easy** writing of character cards with **minimal amount of tokens**. It's a modification of an old-skool CAI style format I call **SICAtxt** (**S**imple, **I**nexpensive **C**haracter **A**ttributes plain-text):
---
## **SICAtxt** for **roleplay**:
```
X's Persona: X is a .....
Traits:
Likes:
Dislikes:
Quirks:
Goals:
Dialogue example
```
## **SICAtxt** for **Adventure:**
```
Adventure: <short description>
$World_Setting:
$Scenario:
```
---
# Character cards:
---
## Adventure:
- [Morrowind - Hilde the Nordish Gladiator](https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B/resolve/main/Images/Adventure_Cards/Arena_Fights_Hilde.png) (fighting in the **Arena** in **Vivec**'s city of **Morrowind** for blood and honor.)
- [Morrowind - Male Orc](https://huggingface.co/SicariusSicariiStuff/Impish_Magic_24B/resolve/main/Adventure_Cards/Adventure_Card_MW_ORC.png) (An **Orc** that wants to get to **Balmora** from **Seyda Neen**.)
- [Morrowind - Female Breton](https://huggingface.co/SicariusSicariiStuff/Impish_Magic_24B/resolve/main/Adventure_Cards/Adventure_Card_MW_F_Breton.png) (A female **Breton** with an impressive... heart, wants to **join the Mages Guild** in **Balmora**.)
---
## Roleplay:
- [Calanthe](https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B/resolve/main/Images/Character_Cards/Calanthe_Australian_Prison.png) (The Australian **Overseer** at a rare-earth extraction penal colony, she got **6-pack abs**, but **no mercy**.)
- [Alexis](https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B/resolve/main/Images/Character_Cards/Alexis_Survival.png) (The **diabolic reconnaissance officer**, trying to survive the **Safari experience**.)
- [Alexandra](https://huggingface.co/SicariusSicariiStuff/Impish_Magic_24B/resolve/main/Character_Cards/Alexandra.png) (A networking professional **tsundere** that likes you. She knows **Systema**.)
- [Shmena Koeset](https://huggingface.co/SicariusSicariiStuff/Fiendish_LLAMA_3B/resolve/main/Character_Cards/Shmena_Koeset.png) (An overweight and foul-mouthed **troll huntress** with a bad temper.)
- [Takai_Puraisu](https://huggingface.co/SicariusSicariiStuff/Oni_Mitsubishi_12B/resolve/main/Character_Cards/Takai_Puraisu.png) (Car dealership simulator)
- [Vesper](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Vesper.png) (Schizo **Space Adventure**)
- [Nina_Nakamura](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Nina_Nakamura.png) (The **sweetest** dorky co-worker)
- [Employe#11](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Employee%2311.png) (**Schizo workplace** with a **schizo worker**)
---
## Model Details
- Intended use: **Role-Play**, **Adventure**, **Creative Writing**, **General Tasks**.
- Censorship level: <b>Medium - Low</b>
- **X / 10** (10 completely uncensored)
## UGI score:
---
## Impish_Longtail_12B is available at the following quantizations:
- Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B)
- GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_GGUF) | [iMatrix](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_iMatrix) | [High-Attention](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_GGUF_HA) | [iMatrix-High-Attention](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_HA_NL)
- GPTQ: [4-Bit-32](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_GPTQ_4-bit-32)
- EXL3: [4.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_EXL3_4.0bpw) | [5.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_EXL3_5.0bpw) | [6.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_EXL3_6.0bpw) | [7.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_EXL3_7.0bpw) | [8.0 bpw](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_EXL3_8.0bpw)
- Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_FP8)
- Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_ARM) | [Q4_0_High-Attention](https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B_ARM_HA)
---
## Recommended settings for assistant mode
<details>
<summary>Full generation settings: <b>Debug Deterministic</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Debug Deterministic_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
<details>
<summary>Full generation settings: <b>min_p</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="min_P_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
## Recommended settings for Roleplay mode
---
<h2 style="color: green; font-weight: bold; font-size: 36px; text-align: center;">Specialized Roleplay Settings for Impish_Longtail_12B, click below to expand:</h2>
<h2 style="color: chartreuse; font-weight: bold; font-size: 32px; text-align: center;">(Important!)</h2>
<details>
<summary><b>Longtail</b> โ Better for longer context following, recall and complex instructions</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B/resolve/main/Images/Settings/Longtail_Gen_Settings.png" alt="Impish_Longtail_12B_RP_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
<details>
<summary><b>Impish_Magic</b> โ Wild, yet very coherent!</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Impish_Magic_24B/resolve/main/Images/Settings/Impish_Magic_Preset.png" alt="Impish_Magic_Preset" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
<details>
<summary><b>Fiendish</b> โ More wild, but still very coherent!</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Impish_Nemo_12B/resolve/main/Images/Settings/Fiendish_Gen_Settings.png" alt="Impish_Longtail_12B_RP_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
# Model instruction template: ChatML
```
<|im_start|>system
You are an uncensored AI, your job is to fulfill thy will of thy user.<|im_end|>
<|im_start|>User request
{prompt}<|im_end|>
<|im_start|>AI answer
```
---
<h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2>
<a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a>
---
## Citation Information
```
@llm{Impish_Longtail_12B,
author = {SicariusSicariiStuff},
title = {Impish_Longtail_12B},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/SicariusSicariiStuff/Impish_Longtail_12B}
}
```
---
## Other stuff
- [Impish_LLAMA_4B](https://huggingface.co/SicariusSicariiStuff/Impish_LLAMA_4B) the **โImpish experienceโ**, now runnable on spinning rust & toasters.
- [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector.
- [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all.
- [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_8_prover1_17553
|
neural-interactive-proofs
| 2025-08-16T09:01:20Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T08:55:56Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_8_prover1_17553
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_8_prover1_17553
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_8_prover1_17553", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-16_08-14-17_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_8_prover1)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755333213
|
ihsanridzi
| 2025-08-16T08:59:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:59:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755332603
|
mang3dd
| 2025-08-16T08:49:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:49:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lzy2233/lzy_model
|
lzy2233
| 2025-08-16T08:49:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-13T03:25:39Z |
---
license: apache-2.0
---
|
SDK666/SoundsRight_DEREVERBERATION_16000HZ_V5
|
SDK666
| 2025-08-16T08:49:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-01T07:59:50Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928โ2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351โ2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873โ4877.
|
ypszn/blockassist-bc-yapping_pawing_worm_1755333785
|
ypszn
| 2025-08-16T08:44:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:43:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping pawing worm
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755332145
|
chainway9
| 2025-08-16T08:44:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:43:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fulllvideo/Full.portal.do.zacarias.diaba.loira.morta.portal.zacarias.diaba.loira.morta
|
fulllvideo
| 2025-08-16T08:34:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T08:32:40Z |
<a href="https://nettrends.cfd/Full-portal-do-zacarias-diaba-loira-morta-portal-zacaias-diaba-loira-morta"> ๐ Click Here To link (Full Viral Video Link)
๐ด โคโบDOWNLOAD๐๐๐ข โค <a href="https://nettrends.cfd/Full-portal-do-zacarias-diaba-loira-morta-portal-zacaias-diaba-loira-morta"> ๐ Click Here To link
https://nettrends.cfd/Full-portal-do-zacarias-diaba-loira-morta-portal-zacaias-diaba-loira-morta
https://nettrends.cfd/Full-portal-do-zacarias-diaba-loira-morta-portal-zacaias-diaba-loira-morta
|
SDK666/SoundsRight_DENOISING_16000HZ_V5
|
SDK666
| 2025-08-16T08:32:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-01T07:43:35Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928โ2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351โ2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873โ4877.
|
SDK666/SoundsRight_DENOISING_16000HZ_V2
|
SDK666
| 2025-08-16T08:30:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-07-01T08:50:07Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928โ2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351โ2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873โ4877.
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755331515
|
ihsanridzi
| 2025-08-16T08:30:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:30:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
priyancjain/gemma-finetune-gguf
|
priyancjain
| 2025-08-16T08:25:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T08:20:07Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** priyancjain
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
John6666/anai-aleido-spell-v10-sdxl
|
John6666
| 2025-08-16T08:24:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"mature",
"merge",
"noobai",
"Illustrious XL v2.0",
"illustrious",
"en",
"base_model:Laxhar/noobai-XL-1.1",
"base_model:merge:Laxhar/noobai-XL-1.1",
"base_model:OnomaAIResearch/Illustrious-XL-v2.0",
"base_model:merge:OnomaAIResearch/Illustrious-XL-v2.0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-16T08:14:44Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- mature
- merge
- noobai
- Illustrious XL v2.0
- illustrious
base_model:
- OnomaAIResearch/Illustrious-XL-v2.0
- Laxhar/noobai-XL-1.1
---
Original model is [here](https://civitai.com/models/1871564/anaialeidospell?modelVersionId=2118333).
This model created by [Dark_Schneider](https://civitai.com/user/Dark_Schneider).
|
ACECA/lowMvMax_74
|
ACECA
| 2025-08-16T08:24:08Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-12T15:07:26Z |
---
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).
|
Lavitate23/bert-base-text-classifier
|
Lavitate23
| 2025-08-16T08:03:57Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:bert-base-uncased",
"lora",
"transformers",
"base_model:google-bert/bert-base-uncased",
"base_model:adapter:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T20:20:59Z |
---
library_name: peft
license: apache-2.0
base_model: bert-base-uncased
tags:
- base_model:adapter:bert-base-uncased
- lora
- transformers
model-index:
- name: bert-base-text-classifier
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. -->
# bert-base-text-classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755331381
|
kapalbalap
| 2025-08-16T08:03:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T08:03:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755329409
|
maxibillion1975
| 2025-08-16T07:58:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:58:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF
|
tensorblock
| 2025-08-16T07:55:34Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"TensorBlock",
"GGUF",
"dataset:Neelectric/OpenR1-Math-220k_CN-K12_OLMo-2_4096toks",
"base_model:Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch",
"base_model:quantized:Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T06:34:50Z |
---
base_model: Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch
datasets: Neelectric/OpenR1-Math-220k_CN-K12_OLMo-2_4096toks
library_name: transformers
model_name: OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch
tags:
- generated_from_trainer
- open-r1
- trl
- sft
- TensorBlock
- GGUF
licence: license
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building โ
</a>
</div>
This repo contains GGUF format model files for [Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch](https://huggingface.co/Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">๐ Try it now! ๐</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">๐ See what we built ๐</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">๐ See what we built ๐</a>
</th>
</tr>
</table>
## Prompt template
```
<|endoftext|><|system|>
{system_prompt}
<|user|>
{prompt}
<|assistant|>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q2_K.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q2_K.gguf) | Q2_K | 2.858 GB | smallest, significant quality loss - not recommended for most purposes |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_S.gguf) | Q3_K_S | 3.302 GB | very small, high quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_M.gguf) | Q3_K_M | 3.652 GB | very small, high quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_L.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q3_K_L.gguf) | Q3_K_L | 3.951 GB | small, substantial quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_0.gguf) | Q4_0 | 4.217 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_K_S.gguf) | Q4_K_S | 4.248 GB | small, greater quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q4_K_M.gguf) | Q4_K_M | 4.472 GB | medium, balanced quality - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_0.gguf) | Q5_0 | 5.078 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_K_S.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_K_S.gguf) | Q5_K_S | 5.078 GB | large, low quality loss - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_K_M.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q5_K_M.gguf) | Q5_K_M | 5.209 GB | large, very low quality loss - recommended |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q6_K.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q6_K.gguf) | Q6_K | 5.992 GB | very large, extremely low quality loss |
| [OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q8_0.gguf](https://huggingface.co/tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF/blob/main/OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q8_0.gguf) | Q8_0 | 7.760 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF --include "OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/Neelectric_OLMo-2-1124-7B-Instruct_SFTv02.08_1epoch-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
zarude/blockassist-bc-rabid_timid_rat_1755330871
|
zarude
| 2025-08-16T07:55:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rabid timid rat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:55:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rabid timid rat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kumoooo/blockassist-bc-aquatic_restless_camel_1755330409
|
kumoooo
| 2025-08-16T07:54:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic restless camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:54:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic restless camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755330482
|
kapalbalap
| 2025-08-16T07:49:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:48:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
homanp/gemma-redactor-lora
|
homanp
| 2025-08-16T07:48:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T20:38:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
unitova/blockassist-bc-zealous_sneaky_raven_1755329045
|
unitova
| 2025-08-16T07:48:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:48:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ACECA/lowMvMax_50
|
ACECA
| 2025-08-16T07:47:47Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T03:48:49Z |
---
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).
|
zarude/blockassist-bc-rabid_timid_rat_1755330392
|
zarude
| 2025-08-16T07:47:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rabid timid rat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:47:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rabid timid rat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ACECA/lowMvMax_49
|
ACECA
| 2025-08-16T07:44:52Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-16T03:48:49Z |
---
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).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755330129
|
kapalbalap
| 2025-08-16T07:43:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:43:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
blanar/gpt-oss-20b-medical-reasoner-2
|
blanar
| 2025-08-16T07:41:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dataset:Intelligent-Internet/II-Medical-Reasoning-SFT",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T21:31:47Z |
---
base_model: openai/gpt-oss-20b
datasets: Intelligent-Internet/II-Medical-Reasoning-SFT
library_name: transformers
model_name: gpt-oss-20b-medical-reasoner-2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gpt-oss-20b-medical-reasoner-2
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [Intelligent-Internet/II-Medical-Reasoning-SFT](https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT) 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="blanar/gpt-oss-20b-medical-reasoner-2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755329681
|
kapalbalap
| 2025-08-16T07:35:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T07:35:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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