modelId
stringlengths 5
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 12:32:32
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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| pipeline_tag
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|---|---|---|---|---|---|---|---|---|---|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1756714210
|
AnerYubo
| 2025-09-01T08:10:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:10:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
itsmanikumar/gpt-oss-20b-multilingual-reasoner
|
itsmanikumar
| 2025-09-01T08:08:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"dataset:HuggingFaceH4/Multilingual-Thinking",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T07:51:15Z |
---
base_model: openai/gpt-oss-20b
datasets: HuggingFaceH4/Multilingual-Thinking
library_name: transformers
model_name: gpt-oss-20b-multilingual-reasoner
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-multilingual-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [HuggingFaceH4/Multilingual-Thinking](https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking) 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="itsmanikumar/gpt-oss-20b-multilingual-reasoner", 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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.9.0.dev20250804+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF
|
mradermacher
| 2025-09-01T08:05:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k",
"base_model:quantized:EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-01T06:52:35Z |
---
base_model: EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## 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/EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-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/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ1_M.gguf) | i1-IQ1_M | 0.5 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q4_0.gguf) | i1-Q4_0 | 1.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q4_1.gguf) | i1-Q4_1 | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k-i1-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-classification-431k.i1-Q6_K.gguf) | i1-Q6_K | 1.5 | 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 -->
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756713850
|
Ferdi3425
| 2025-09-01T08:05:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:05:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
betreosi/blockassist-bc-stinging_prowling_lion_1756713877
|
betreosi
| 2025-09-01T08:05:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging prowling lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:05:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging prowling lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
faisu-eth/blockassist-bc-thick_twitchy_jackal_1756713764
|
faisu-eth
| 2025-09-01T08:03:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick twitchy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T08:03:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick twitchy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Nerva1228/gushinn
|
Nerva1228
| 2025-09-01T07:57:36Z | 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
| 2025-09-01T07:57:35Z |
---
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: gushinn
---
# Gushinn
<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 `gushinn` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "gushinn",
"lora_weights": "https://huggingface.co/Nerva1228/gushinn/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Nerva1228/gushinn', weight_name='lora.safetensors')
image = pipeline('gushinn').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/Nerva1228/gushinn/discussions) to add images that show off what you’ve made with this LoRA.
|
david3621/blockassist-bc-gentle_meek_cat_1756712133
|
david3621
| 2025-09-01T07:54:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle meek cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:51:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle meek cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
varshithkumar/wbc_resnet50
|
varshithkumar
| 2025-09-01T07:49:11Z | 9 | 0 |
keras
|
[
"keras",
"tf-keras",
"tensorflow",
"image-classification",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2025-08-29T13:41:33Z |
---
---
pipeline_tag: image-classification
tags:
- keras
- tensorflow
- image-classification
license: apache-2.0
---
# WBC ResNet50
This is a ResNet50 model trained on WBC dataset using Keras.
|
Wan-AI/Wan2.2-S2V-14B
|
Wan-AI
| 2025-09-01T07:48:57Z | 10,708 | 213 |
diffusers
|
[
"diffusers",
"safetensors",
"s2v",
"other",
"arxiv:2508.18621",
"arxiv:2503.20314",
"license:apache-2.0",
"region:us"
] |
other
| 2025-08-25T02:38:55Z |
---
license: apache-2.0
pipeline_tag: other
library_name: diffusers
---
# Wan2.2-S2V-14B: Audio-Driven Cinematic Video Generation
This repository features the **Wan2.2-S2V-14B** model, designed for audio-driven cinematic video generation. It was introduced in the paper:
[**Wan-S2V: Audio-Driven Cinematic Video Generation**](https://huggingface.co/papers/2508.18621)
<p align="center">
<img src="assets/logo.png" width="400"/>
<p>
<p align="center">
💜 <a href="https://wan.video"><b>Wan Homepage</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face Organization</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope Organization</a>   |    📑 <a href="https://huggingface.co/papers/2508.18621">Wan-S2V Paper</a>    |    📑 <a href="https://arxiv.org/abs/2503.20314">Wan2.2 Base Paper</a>    | 🌐 <a href="https://humanaigc.github.io/wan-s2v-webpage">Project Page</a>    |    📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a>    |    💬 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>  
<br>
📕 <a href="https://alidocs.dingtalk.com/i/nodes/jb9Y4gmKWrx9eo4dCql9LlbYJGXn6lpz">使用指南(中文)</a>   |    📘 <a href="https://alidocs.dingtalk.com/i/nodes/EpGBa2Lm8aZxe5myC99MelA2WgN7R35y">User Guide(English)</a>   |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat(微信)</a>  
<br>
## Abstract (Wan-S2V Paper)
Current state-of-the-art (SOTA) methods for audio-driven character animation demonstrate promising performance for scenarios primarily involving speech and singing. However, they often fall short in more complex film and television productions, which demand sophisticated elements such as nuanced character interactions, realistic body movements, and dynamic camera work. To address this long-standing challenge of achieving film-level character animation, we propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan. Our model achieves significantly enhanced expressiveness and fidelity in cinematic contexts compared to existing approaches. We conducted extensive experiments, benchmarking our method against cutting-edge models such as Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate that our approach significantly outperforms these existing solutions. Additionally, we explore the versatility of our method through its applications in long-form video generation and precise video lip-sync editing.
-----
[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <br>
We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
- 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
- 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
- 👍 **Efficient High-Definition Hybrid TI2V**: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously.
## Video Demos
<div align="center">
<video width="80%" controls>
<source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
## 🔥 Latest News!!
* Aug 26, 2025: 🎵 We introduce **[Wan2.2-S2V-14B](https://humanaigc.github.io/wan-s2v-webpage)**, an audio-driven cinematic video generation model, including [inference code](#run-speech-to-video-generation), [model weights](#model-download), and [technical report](https://humanaigc.github.io/wan-s2v-webpage/content/wan-s2v.pdf)! Now you can try it on [wan.video](https://wan.video/), [ModelScope Gradio](https://www.modelscope.cn/studios/Wan-AI/Wan2.2-S2V) or [HuggingFace Gradio](https://huggingface.co/spaces/Wan-AI/Wan2.2-S2V)!
* Jul 28, 2025: 👋 We have open a [HF space](https://huggingface.co/spaces/Wan-AI/Wan-2.2-5B) using the TI2V-5B model. Enjoy!
* Jul 28, 2025: 👋 Wan2.2 has been integrated into ComfyUI ([CN](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2) | [EN](https://docs.comfy.org/tutorials/video/wan/wan2_2)). Enjoy!
* Jul 28, 2025: 👋 Wan2.2's T2V, I2V and TI2V have been integrated into Diffusers ([T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) | [I2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) | [TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)). Feel free to give it a try!
* Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
## Community Works
If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or [**Wan2.2**](https://github.com/Wan-Video/Wan2.2), and you would like more people to see it, please inform us.
- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides comprehensive support for Wan 2.2, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training.
- [Kijai's ComfyUI WanVideoWrapper](https://github.com/kijai/ComfyUI-WanVideoWrapper) is an alternative implementation of Wan models for ComfyUI. Thanks to its Wan-only focus, it's on the frontline of getting cutting edge optimizations and hot research features, which are often hard to integrate into ComfyUI quickly due to its more rigid structure.
## 📑 Todo List
- Wan2.2-S2V Speech-to-Video
- [x] Inference code of Wan2.2-S2V
- [x] Checkpoints of Wan2.2-S2V-14B
- [ ] ComfyUI integration
- [ ] Diffusers integration
## Run Wan2.2
#### Installation
Clone the repo:
```sh
git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2
```
Install dependencies:
```sh
# Ensure torch >= 2.4.0
# If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last
pip install -r requirements.txt
```
#### Model Download
| Models | Download Links | Description |
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
| T2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | Text-to-Video MoE model, supports 480P & 720P |
| I2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | Image-to-Video MoE model, supports 480P & 720P |
| TI2V-5B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | High-compression VAE, T2V+I2V, supports 720P |
| S2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-S2V-14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | Speech-to-Video model, supports 480P & 720P |
Download models using huggingface-cli:
``` sh
pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.2-S2V-14B --local-dir ./Wan2.2-S2V-14B
```
Download models using modelscope-cli:
``` sh
pip install modelscope
modelscope download Wan-AI/Wan2.2-S2V-14B --local_dir ./Wan2.2-S2V-14B
```
#### Run Speech-to-Video Generation
This repository supports the `Wan2.2-S2V-14B` Speech-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.
- Single-GPU Speech-to-Video inference
```sh
python generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --offload_model True --convert_model_dtype --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav"
# Without setting --num_clip, the generated video length will automatically adjust based on the input audio length
```
> 💡 This command can run on a GPU with at least 80GB VRAM.
- Multi-GPU inference using FSDP + DeepSpeed Ulysses
```sh
torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav"
```
- Pose + Audio driven generation
```sh
torchrun --nproc_per_node=8 generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "a person is singing" --image "examples/pose.png" --audio "examples/sing.MP3" --pose_video "./examples/pose.mp4"
```
> 💡For the Speech-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
> 💡The model can generate videos from audio input combined with reference image and optional text prompt.
> 💡The `--pose_video` parameter enables pose-driven generation, allowing the model to follow specific pose sequences while generating videos synchronized with audio input.
> 💡The `--num_clip` parameter controls the number of video clips generated, useful for quick preview with shorter generation time.
## Computational Efficiency on Different GPUs
We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
<div align="center">
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
</div>
> The parameter settings for the tests presented in this table are as follows:
> (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu`
(--convert_model_dtype converts model parameter types to config.param_dtype);
> (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
> (3) Tests were run without the `--use_prompt_extend` flag;
> (4) Reported results are the average of multiple samples taken after the warm-up phase.
-------
## Introduction of Wan2.2
**Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.
##### (1) Mixture-of-Experts (MoE) Architecture
Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
<div align="center">
<img src="assets/moe_arch.png" alt="" style="width: 90%;" />
</div>
The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
<div align="center">
<img src="assets/moe_2.png" alt="" style="width: 90%;" />
</div>
To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.
##### (2) Efficient High-Definition Hybrid TI2V
To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
<div align="center">
<img src="assets/vae.png" alt="" style="width: 80%;" />
</div>
##### Comparisons to SOTAs
We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
<div align="center">
<img src="assets/performance.png" alt="" style="width: 90%;" />
</div>
## Citation
If you find our work helpful, please cite us.
```
@article{wan2025,
title={Wan: Open and Advanced Large-Scale Video Generative Models},
author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
journal = {arXiv preprint arXiv:2503.20314},
year={2025}
}
@article{wan2025s2v,
title={Wan-S2V:Audio-Driven Cinematic Video Generation},
author={Xin Gao, Li Hu, Siqi Hu, Mingyang Huang, Chaonan Ji, Dechao Meng, Jinwei Qi, Penchong Qiao, Zhen Shen, Yafei Song, Ke Sun, Linrui Tian, Guangyuan Wang, Qi Wang, Zhongjian Wang, Jiayu Xiao, Sheng Xu, Bang Zhang, Peng Zhang, Xindi Zhang, Zhe Zhang, Jingren Zhou, Lian Zhuo},
journal={arXiv preprint arXiv:2508.18621},
year={2025}
}
```
## License Agreement
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
## Acknowledgements
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
## Contact Us
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756712662
|
Ferdi3425
| 2025-09-01T07:45:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:45:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_399
|
AnonymousCS
| 2025-09-01T07:44:57Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"rembert",
"text-classification",
"generated_from_trainer",
"base_model:google/rembert",
"base_model:finetune:google/rembert",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-31T21:49:19Z |
---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_399
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_399
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6043
- Accuracy: 0.9498
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.3688 | 1.0 | 130 | 0.6023 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1849 | 2.0 | 260 | 0.6466 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.8075 | 3.0 | 390 | 0.6043 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Yuchan5386/SmoliteXL-2
|
Yuchan5386
| 2025-09-01T07:40:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-01T07:40:19Z |
---
license: apache-2.0
---
|
AnonymousCS/populism_classifier_398
|
AnonymousCS
| 2025-09-01T07:38:29Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"rembert",
"text-classification",
"generated_from_trainer",
"base_model:google/rembert",
"base_model:finetune:google/rembert",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-31T21:41:45Z |
---
library_name: transformers
license: apache-2.0
base_model: google/rembert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_398
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_398
This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6761
- Accuracy: 0.9226
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.5666 | 1.0 | 88 | 0.6764 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.5654 | 2.0 | 176 | 0.6810 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6907 | 3.0 | 264 | 0.6758 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4527 | 4.0 | 352 | 0.6837 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.6271 | 5.0 | 440 | 0.6761 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
walbosui/blockassist-bc-miniature_playful_walrus_1756712234
|
walbosui
| 2025-09-01T07:38:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature playful walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:37:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature playful walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zaydzuhri/top-340M-4096-model
|
zaydzuhri
| 2025-09-01T07:37:55Z | 22 | 0 | null |
[
"safetensors",
"top_transformer",
"arxiv:2508.19228",
"arxiv:1910.09700",
"region:us"
] | null | 2025-09-01T07:07:58Z |
# This model is used in arxiv.org/abs/2508.19228
# Token Order Prediction
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[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]
|
zaydzuhri/vanilla-340M-4096-model
|
zaydzuhri
| 2025-09-01T07:37:03Z | 114 | 0 | null |
[
"safetensors",
"transformer",
"arxiv:2504.20966",
"arxiv:2508.19228",
"region:us"
] | null | 2025-04-21T07:15:55Z |
# This model is from the paper arxiv.org/abs/2504.20966
# Softpick: No Attention Sink, No Massive Activations with Rectified Softmax
# Also used in arxiv.org/abs/2508.19228
# Token Order Prediction
See code: https://github.com/zaydzuhri/softpick-attention
This model is only usable through these repositories:
https://github.com/zaydzuhri/flash-linear-attention/tree/softpick-attention
https://github.com/zaydzuhri/flame/tree/softpick-attention
|
TryCAEAIXR/gemma-3-270m-it-blr-slang
|
TryCAEAIXR
| 2025-09-01T07:27:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T06:00:08Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-blr-slang
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-3-270m-it-blr-slang
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="TryCAEAIXR/gemma-3-270m-it-blr-slang", 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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756711582
|
2hpsatt
| 2025-09-01T07:27:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:27:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756711000
|
arif696
| 2025-09-01T07:18:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:17:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AssanaliAidarkhan/qwen-medical-rag
|
AssanaliAidarkhan
| 2025-09-01T07:17:08Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-31T11:08:03Z |
---
title: Qwen Medical RAG System
emoji: 🏥
colorFrom: green
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
---
# Qwen Medical RAG System
Medical advisory system using Qwen 1.5 0.5B for ACL injury analysis.
## Knowledge Base Categories
This system provides advice for:
- `partial_acl_injury` - Partial ACL damage with some intact fibers
- `partial_acl_fiber_disruption` - Partial fiber disruption requiring evaluation
- `complete_acl_tear` - Complete ACL rupture requiring surgery
- `acl_sprain` - ACL strain with conservative treatment
## Files
- `medical_knowledge.json`: ACL medical knowledge base (4 categories)
- `rag_config.json`: System configuration
## Disclaimer
For research and educational purposes only. Not for clinical diagnosis.
Always consult qualified medical professionals.
|
canvascomputing/malwi
|
canvascomputing
| 2025-09-01T07:13:18Z | 0 | 0 | null |
[
"safetensors",
"distilbert",
"arxiv:2404.04991",
"arxiv:2504.14886",
"license:mit",
"region:us"
] | null | 2025-09-01T07:11:08Z |
---
license: mit
---
# malwi - AI Python Malware Scanner
<img src="malwi-logo.png" alt="Logo">
## malwi specializes in finding malware
### Key Features
- 🛡️ **AI-Powered Python Malware Detection**: Leverages advanced AI to identify malicious code in Python projects with high accuracy.
- ⚡ **Lightning-Fast Codebase Scanning**: Scans entire repositories in seconds, so you can focus on development—not security worries.
- 🔒 **100% Offline & Private**: Your code never leaves your machine. Full control, zero data exposure.
- 💰 **Free & Open-Source**: No hidden costs. Built on transparent research and openly available data.
- 🇪🇺 **Developed in the EU**: Committed to open-source principles and European data standards.
### 1) Install
```
pip install --user malwi
```
### 2) Run
```bash
malwi scan examples/malicious
```
### 3) Evaluate: a [recent zero-day](https://socket.dev/blog/malicious-pypi-package-targets-discord-developers-with-RAT) detected with high confidence
```
__ __
.--------.---.-| .--.--.--|__|
| | _ | | | | | |
|__|__|__|___._|__|________|__|
AI Python Malware Scanner
- target: examples
- seconds: 1.87
- files: 14
├── scanned: 4 (.py)
├── skipped: 10 (.cfg, .md, .toml, .txt)
└── suspicious:
├── examples/malicious/discordpydebug-0.0.4/setup.py
│ └── <module>
│ ├── archive compression
│ └── package installation execution
└── examples/malicious/discordpydebug-0.0.4/src/discordpydebug/__init__.py
├── <module>
│ ├── process management
│ ├── deserialization
│ ├── system interaction
│ └── user io
├── run
│ └── fs linking
├── debug
│ ├── fs linking
│ └── archive compression
└── runcommand
└── process management
=> 👹 malicious 0.98
```
## PyPI Package Scanning
malwi can directly scan PyPI packages without executing malicious logic, typically placed in `setup.py` or `__init__.py` files:
```bash
malwi pypi requests
````
```
__ __
.--------.---.-| .--.--.--|__|
| | _ | | | | | |
|__|__|__|___._|__|________|__|
AI Python Malware Scanner
- target: downloads/requests-2.32.4.tar
- seconds: 3.10
- files: 84
├── scanned: 34
└── skipped: 50
=> 🟢 good
```
## Python API
malwi provides a comprehensive Python API for integrating malware detection into your applications.
### Quick Start
```python
import malwi
report = malwi.MalwiReport.create(input_path="suspicious_file.py")
for obj in report.malicious_objects:
print(f"File: {obj.file_path}")
```
### `MalwiReport`
```python
MalwiReport.create(
input_path, # str or Path - file/directory to scan
accepted_extensions=None, # List[str] - file extensions to scan (e.g., ['py', 'js'])
silent=False, # bool - suppress progress messages
malicious_threshold=0.7, # float - threshold for malicious classification (0.0-1.0)
on_finding=None # callable - callback when malicious objects found
) -> MalwiReport # Returns: MalwiReport instance with scan results
```
```python
import malwi
report = malwi.MalwiReport.create("suspicious_directory/")
# Properties
report.malicious # bool: True if malicious objects detected
report.confidence # float: Overall confidence score (0.0-1.0)
report.duration # float: Scan duration in seconds
report.all_objects # List[MalwiObject]: All analyzed code objects
report.malicious_objects # List[MalwiObject]: Objects exceeding threshold
report.threshold # float: Maliciousness threshold used (0.0-1.0)
report.all_files # List[Path]: All files found in input path
report.skipped_files # List[Path]: Files skipped (wrong extension)
report.processed_files # int: Number of files successfully processed
report.activities # List[str]: Suspicious activities detected
report.input_path # str: Original input path scanned
report.start_time # str: ISO 8601 timestamp when scan started
report.all_file_types # List[str]: All file extensions found
report.version # str: Malwi version with model hash
# Methods
report.to_demo_text() # str: Human-readable tree summary
report.to_json() # str: JSON formatted report
report.to_yaml() # str: YAML formatted report
report.to_markdown() # str: Markdown formatted report
# Pre-load models to avoid delay on first prediction
malwi.MalwiReport.load_models_into_memory()
```
### `MalwiObject`
```python
obj = report.all_objects[0]
# Core properties
obj.name # str: Function/class/module name
obj.file_path # str: Path to source file
obj.language # str: Programming language ('python'/'javascript')
obj.maliciousness # float|None: ML confidence score (0.0-1.0)
obj.warnings # List[str]: Compilation warnings/errors
# Source code and AST compilation
obj.file_source_code # str: Complete content of source file
obj.source_code # str|None: Extracted source for this specific object
obj.byte_code # List[Instruction]|None: Compiled AST bytecode
obj.location # Tuple[int,int]|None: Start and end line numbers
obj.embedding_count # int: Number of DistilBERT tokens (cached)
# Analysis methods
obj.predict() # dict: Run ML prediction and update maliciousness
obj.to_tokens() # List[str]: Extract tokens for analysis
obj.to_token_string() # str: Space-separated token string
obj.to_string() # str: Bytecode as readable string
obj.to_hash() # str: SHA256 hash of bytecode
obj.to_dict() # dict: Serializable representation
obj.to_yaml() # str: YAML formatted output
obj.to_json() # str: JSON formatted output
# Class methods
MalwiObject.all_tokens(language="python") # List[str]: All possible tokens
```
## Why malwi?
Malicious actors are increasingly [targeting open-source projects](https://arxiv.org/pdf/2404.04991), introducing packages designed to compromise security.
Common malicious behaviors include:
- **Data exfiltration**: Theft of sensitive information such as credentials, API keys, or user data.
- **Backdoors**: Unauthorized remote access to systems, enabling attackers to exploit vulnerabilities.
- **Destructive actions**: Deliberate sabotage, including file deletion, database corruption, or application disruption.
## How does it work?
malwi is based on the design of [_Zero Day Malware Detection with Alpha: Fast DBI with Transformer Models for Real World Application_ (2025)](https://arxiv.org/pdf/2504.14886v1).
Imagine there is a function like:
```python
def runcommand(value):
output = subprocess.run(value, shell=True, capture_output=True)
return [output.stdout, output.stderr]
```
### 1. Files are compiled to create an Abstract Syntax Tree with [Tree-sitter](https://tree-sitter.github.io/tree-sitter/index.html)
```
module [0, 0] - [3, 0]
function_definition [0, 0] - [2, 41]
name: identifier [0, 4] - [0, 14]
parameters: parameters [0, 14] - [0, 21]
identifier [0, 15] - [0, 20]
...
```
### 2. The AST is transpiled to dummy bytecode
The bytecode is enhanced with security related instructions.
```
TARGETED_FILE PUSH_NULL LOAD_GLOBAL PROCESS_MANAGEMENT LOAD_ATTR run LOAD_PARAM value LOAD_CONST BOOLEAN LOAD_CONST BOOLEAN KW_NAMES shell capture_output CALL STRING_VERSION STORE_GLOBAL output LOAD_GLOBAL output LOAD_ATTR stdout LOAD_GLOBAL output LOAD_ATTR stderr BUILD_LIST STRING_VERSION RETURN_VALUE
```
### 3. The bytecode is fed into a pre-trained [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)
A DistilBERT model trained on [malware-samples](https://github.com/schirrmacher/malwi-samples) is used to identify suspicious code patterns.
```
=> Maliciousness: 0.98
```
## Benchmarks?
```
training_loss: 0.0110
epochs_completed: 3.0000
original_train_samples: 598540.0000
windowed_train_features: 831865.0000
original_validation_samples: 149636.0000
windowed_validation_features: 204781.0000
benign_samples_used: 734930.0000
malicious_samples_used: 13246.0000
benign_to_malicious_ratio: 60.0000
vocab_size: 30522.0000
max_length: 512.0000
window_stride: 128.0000
batch_size: 16.0000
eval_loss: 0.0107
eval_accuracy: 0.9980
eval_f1: 0.9521
eval_precision: 0.9832
eval_recall: 0.9229
eval_runtime: 115.5982
eval_samples_per_second: 1771.4900
eval_steps_per_second: 110.7200
epoch: 3.0000
```
## Contributing & Support
- Found a bug or have a feature request? [Open an issue](https://github.com/schirrmacher/malwi/issues).
- Do you have access to malicious packages in Rust, Go, or other languages? [Contact via GitHub profile](https://github.com/schirrmacher).
- Struggling with false-positive findings? [Create a Pull-Request](https://github.com/schirrmacher/malwi-samples/pulls).
## Research
### Prerequisites
1. **Package Manager**: Install [uv](https://docs.astral.sh/uv/) for fast Python dependency management
2. **Training Data**: The research CLI will automatically clone [malwi-samples](https://github.com/schirrmacher/malwi-samples) when needed
### Quick Start
```bash
# Install dependencies
uv sync
# Run tests
uv run pytest tests
# Train a model from scratch (full pipeline with automatic data download)
./research download preprocess train
```
#### Individual Pipeline Steps
```bash
# 1. Download training data (clones malwi-samples + downloads repositories)
./research download
# 2. Data preprocessing only (parallel processing, ~4 min on 32 cores)
./research preprocess --language python
# 3. Model training only (tokenizer + DistilBERT, ~40 minutes on NVIDIA RTX 4090)
./research train
```
## Limitations
The malicious dataset includes some boilerplate functions, such as setup functions, which can also appear in benign code. These cause false positives during scans. The goal is to triage and reduce such false positives to improve malwi's accuracy.
## What's next?
The first iteration focuses on **maliciousness of Python source code**.
Future iterations will cover malware scanning for more languages (JavaScript, Rust, Go) and more formats (binaries, logs).
|
karinegabsschon/BERTopic_Environmental
|
karinegabsschon
| 2025-09-01T07:13:11Z | 2 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2025-07-07T16:53:11Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# BERTopic_Environmental
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Environmental")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 26
* Number of training documents: 905
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | electric - car - cars - charging - vehicles | 11 | -1_electric_car_cars_charging |
| 0 | battery - batteries - lithium - catl - technology | 213 | 0_battery_batteries_lithium_catl |
| 1 | byd - charging - dolphin - chinese - new | 61 | 1_byd_charging_dolphin_chinese |
| 2 | charging - ev - chargers - ev charging - electric | 58 | 2_charging_ev_chargers_ev charging |
| 3 | zero - government - uk - mandate - electric | 57 | 3_zero_government_uk_mandate |
| 4 | electric - charging - points - france - car | 49 | 4_electric_charging_points_france |
| 5 | battery - lithium - recycling - batteries - supply | 48 | 5_battery_lithium_recycling_batteries |
| 6 | cars - combustion - study - electric - car | 36 | 6_cars_combustion_study_electric |
| 7 | percent - cars - market - sales - vehicles | 33 | 7_percent_cars_market_sales |
| 8 | fires - safety - battery - electric - cars | 29 | 8_fires_safety_battery_electric |
| 9 | charging - electric - sweden - vehicles - circle | 29 | 9_charging_electric_sweden_vehicles |
| 10 | tax - drivers - petrol - ev - rates | 25 | 10_tax_drivers_petrol_ev |
| 11 | kia - car - model - electric - range | 25 | 11_kia_car_model_electric |
| 12 | cent - car - petrol - evs - drivers | 23 | 12_cent_car_petrol_evs |
| 13 | charging - stations - charging stations - charging points - points | 23 | 13_charging_stations_charging stations_charging points |
| 14 | india - ev - green - mobility - electric | 23 | 14_india_ev_green_mobility |
| 15 | indonesia - battery - lg - ev - ev battery | 20 | 15_indonesia_battery_lg_ev |
| 16 | department - flames - police - car - tesla | 20 | 16_department_flames_police_car |
| 17 | transport - ireland - council - ev - climate | 19 | 17_transport_ireland_council_ev |
| 18 | toyota - electric - new - europe - hyundai | 19 | 18_toyota_electric_new_europe |
| 19 | sales - new - electric - cent - car | 17 | 19_sales_new_electric_cent |
| 20 | european - commission - eu - von - der | 15 | 20_european_commission_eu_von |
| 21 | power - blackout - spain - homes - electricity | 14 | 21_power_blackout_spain_homes |
| 22 | nissan - leaf - micra - new - generation | 13 | 22_nissan_leaf_micra_new |
| 23 | ship - coast - vessel - coast guard - guard | 13 | 23_ship_coast_vessel_coast guard |
| 24 | id - volkswagen - vw - every1 - id every1 | 12 | 24_id_volkswagen_vw_every1 |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 2.0.2
* HDBSCAN: 0.8.40
* UMAP: 0.5.8
* Pandas: 2.2.2
* Scikit-Learn: 1.6.1
* Sentence-transformers: 4.1.0
* Transformers: 4.53.0
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.11.13
|
VoilaRaj/81_g_V5HwwQ
|
VoilaRaj
| 2025-09-01T07:12:39Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-01T07:12:05Z |
---
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).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756710612
|
akirafudo
| 2025-09-01T07:11:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:10:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
david3621/blockassist-bc-gentle_meek_cat_1756709671
|
david3621
| 2025-09-01T07:10:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle meek cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:09:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle meek cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karinegabsschon/BERTopic_Political
|
karinegabsschon
| 2025-09-01T07:09:04Z | 2 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2025-07-07T16:42:01Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# BERTopic_Political
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Political")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 20
* Number of training documents: 619
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | electric - tariffs - vehicles - ev - car | 11 | -1_electric_tariffs_vehicles_ev |
| 0 | cars - spd - tax - electric - purchase | 97 | 0_cars_spd_tax_electric |
| 1 | charging - chargers - public - ev - points | 87 | 1_charging_chargers_public_ev |
| 2 | tax - car - new - electric - petrol | 72 | 2_tax_car_new_electric |
| 3 | tesla - musk - elon - elon musk - trump | 53 | 3_tesla_musk_elon_elon musk |
| 4 | moves - aid - electric - euros - plan | 49 | 4_moves_aid_electric_euros |
| 5 | byd - chinese - china - price - price war | 36 | 5_byd_chinese_china_price |
| 6 | targets - government - mandate - starmer - zero | 25 | 6_targets_government_mandate_starmer |
| 7 | euros - bonus - ecological - ecological bonus - electric | 21 | 7_euros_bonus_ecological_ecological bonus |
| 8 | california - trump - states - administration - electric | 21 | 8_california_trump_states_administration |
| 9 | tariffs - united states - united - states - plant | 20 | 9_tariffs_united states_united_states |
| 10 | ukraine - region - electric - ukrainian - vehicles | 18 | 10_ukraine_region_electric_ukrainian |
| 11 | tesla - city - toronto - canadian - chow | 16 | 11_tesla_city_toronto_canadian |
| 12 | eu - china - chinese - tariffs - minimum | 15 | 12_eu_china_chinese_tariffs |
| 13 | chinese - defence - security - spying - military | 15 | 13_chinese_defence_security_spying |
| 14 | european - eu - commission - industry - electric | 14 | 14_european_eu_commission_industry |
| 15 | huf - businesses - subsidies - hungary - battery | 13 | 15_huf_businesses_subsidies_hungary |
| 16 | cent - government - diesel - fleet - electric | 12 | 16_cent_government_diesel_fleet |
| 17 | credit - tax - electric - vehicles - electric vehicles | 12 | 17_credit_tax_electric_vehicles |
| 18 | british - trade - cars - government - tariffs | 12 | 18_british_trade_cars_government |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 2.0.2
* HDBSCAN: 0.8.40
* UMAP: 0.5.8
* Pandas: 2.2.2
* Scikit-Learn: 1.6.1
* Sentence-transformers: 4.1.0
* Transformers: 4.53.0
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.11.13
|
vendi11/blockassist-bc-placid_placid_llama_1756710490
|
vendi11
| 2025-09-01T07:08:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T07:08:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hogensynoo/blockassist-bc-wary_darting_platypus_1756708124
|
hogensynoo
| 2025-09-01T06:28:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wary darting platypus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T06:28:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wary darting platypus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/FoxCide-12B-Forgottenslop-Mell-i1-GGUF
|
mradermacher
| 2025-09-01T06:27:59Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-01T06:05:49Z |
<!-- ### 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/pot99rta/FoxCide-12B-Forgottenslop-Mell
|
outlookAi/Xg4E2wMoPV
|
outlookAi
| 2025-09-01T06:25:39Z | 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
| 2025-09-01T06:08:44Z |
---
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: Kaong
---
# Xg4E2Wmopv
<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 `Kaong ` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Kaong ",
"lora_weights": "https://huggingface.co/outlookAi/Xg4E2wMoPV/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('outlookAi/Xg4E2wMoPV', weight_name='lora.safetensors')
image = pipeline('Kaong ').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: 1200
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/outlookAi/Xg4E2wMoPV/discussions) to add images that show off what you’ve made with this LoRA.
|
the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00160
|
the-acorn-ai
| 2025-09-01T06:24:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"spiral",
"self-play",
"reinforcement-learning",
"octothinker",
"multi-agent",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T06:23:59Z |
---
base_model: OctoThinker-8B
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- spiral
- self-play
- reinforcement-learning
- octothinker
- multi-agent
---
# SPIRAL OctoThinker-8B Multi-Agent Model
This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.
## Model Details
- **Base Model**: OctoAI/OctoThinker-8B
- **Training Framework**: SPIRAL
- **Checkpoint**: step_00160
- **Model Size**: 8B parameters
- **Training Date**: 2025-08-31
## Training Configuration
The model was trained with self-play on multiple environments:
- KuhnPoker-v1
- TicTacToe-v0
- SimpleNegotiation-v1
### Training Parameters
```json
{
"learning_rate": "1e-6",
"train_batch_size": 128,
"num_ppo_epochs": 2,
"temperature": 1.0,
"max_model_len": 16384,
"environments": [
"KuhnPoker-v1",
"TicTacToe-v0",
"SimpleNegotiation-v1"
],
"base_model": "OctoThinker-8B",
"framework": "SPIRAL"
}
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00160")
model = AutoModelForCausalLM.from_pretrained(
"the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00160",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## License
This model is licensed under the Apache License 2.0.
|
the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00128
|
the-acorn-ai
| 2025-09-01T06:23:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"spiral",
"self-play",
"reinforcement-learning",
"octothinker",
"multi-agent",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T06:23:11Z |
---
base_model: OctoThinker-8B
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- spiral
- self-play
- reinforcement-learning
- octothinker
- multi-agent
---
# SPIRAL OctoThinker-8B Multi-Agent Model
This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.
## Model Details
- **Base Model**: OctoAI/OctoThinker-8B
- **Training Framework**: SPIRAL
- **Checkpoint**: step_00128
- **Model Size**: 8B parameters
- **Training Date**: 2025-08-31
## Training Configuration
The model was trained with self-play on multiple environments:
- KuhnPoker-v1
- TicTacToe-v0
- SimpleNegotiation-v1
### Training Parameters
```json
{
"learning_rate": "1e-6",
"train_batch_size": 128,
"num_ppo_epochs": 2,
"temperature": 1.0,
"max_model_len": 16384,
"environments": [
"KuhnPoker-v1",
"TicTacToe-v0",
"SimpleNegotiation-v1"
],
"base_model": "OctoThinker-8B",
"framework": "SPIRAL"
}
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00128")
model = AutoModelForCausalLM.from_pretrained(
"the-acorn-ai/spiral-octothinker-8b-multi-three-games-step00128",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## License
This model is licensed under the Apache License 2.0.
|
the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00224
|
the-acorn-ai
| 2025-09-01T06:22:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"spiral",
"self-play",
"reinforcement-learning",
"multi-agent",
"conversational",
"en",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T06:22:13Z |
---
base_model: Qwen/Qwen3-8B-Base
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- spiral
- self-play
- reinforcement-learning
- qwen3
- multi-agent
---
# SPIRAL Qwen3-8B Multi-Agent Model
This model was trained using the SPIRAL (Self-Play Iterative Reinforcement learning for Adaptation and Learning) framework.
## Model Details
- **Base Model**: Qwen/Qwen3-8B-Base
- **Training Framework**: SPIRAL
- **Checkpoint**: step_00224
- **Model Size**: 8B parameters
- **Training Date**: 2025-08-31
## Training Configuration
The model was trained with self-play on multiple environments:
- KuhnPoker-v1
- TicTacToe-v0
- SimpleNegotiation-v1
### Training Parameters
```json
{
"learning_rate": "1e-6",
"train_batch_size": 128,
"num_ppo_epochs": 2,
"temperature": 1.0,
"max_model_len": 16384,
"environments": [
"KuhnPoker-v1",
"TicTacToe-v0",
"SimpleNegotiation-v1"
],
"base_model": "Qwen/Qwen3-8B-Base",
"framework": "SPIRAL"
}
```
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00224")
model = AutoModelForCausalLM.from_pretrained(
"the-acorn-ai/spiral-qwen-8b-khun-tictactoe-8k-step00224",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## License
This model is licensed under the Apache License 2.0.
|
0xlich/task-13-Qwen-Qwen2.5-1.5B-Instruct
|
0xlich
| 2025-09-01T06:22:44Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"region:us"
] | null | 2025-09-01T04:34:48Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2
|
Bingham/qwen_2_5_grpo_11_train_unsloth_model
|
Bingham
| 2025-09-01T06:20:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-26T19:28:04Z |
---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Bingham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
binhpdt/reproduced-gliner-medium
|
binhpdt
| 2025-09-01T06:20:22Z | 0 | 0 | null |
[
"pytorch",
"base_model:urchade/gliner_base",
"base_model:finetune:urchade/gliner_base",
"license:apache-2.0",
"region:us"
] | null | 2025-09-01T06:05:20Z |
---
license: apache-2.0
base_model:
- urchade/gliner_base
---
Reproducing training the original model of GLiNER for research purpose.
The model is trained with author's hyper-params, the batch size is 8 and the step is 30k.
|
mradermacher/Epstein-i1-GGUF
|
mradermacher
| 2025-09-01T06:20:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Pclanglais/Epstein",
"base_model:quantized:Pclanglais/Epstein",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-01T05:12:36Z |
---
base_model: Pclanglais/Epstein
language:
- en
library_name: transformers
license: cc-by-sa-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/Pclanglais/Epstein
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Epstein-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Epstein-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/Epstein-i1-GGUF/resolve/main/Epstein.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q4_1.gguf) | i1-Q4_1 | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-i1-GGUF/resolve/main/Epstein.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | 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 -->
|
mradermacher/Epstein-GGUF
|
mradermacher
| 2025-09-01T06:12:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Pclanglais/Epstein",
"base_model:quantized:Pclanglais/Epstein",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T15:46:40Z |
---
base_model: Pclanglais/Epstein
language:
- en
library_name: transformers
license: cc-by-sa-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Pclanglais/Epstein
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Epstein-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Epstein-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Epstein-GGUF/resolve/main/Epstein.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
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 -->
|
LarryAIDraw/checkpoint-e18_s882
|
LarryAIDraw
| 2025-09-01T06:11:12Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-09-01T06:07:55Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/1908769/augusta-wuthering-waves
|
kejuss/blockassist-bc-timid_voracious_gecko_1756706872
|
kejuss
| 2025-09-01T06:08:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid voracious gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T06:08:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid voracious gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756705660
|
Sayemahsjn
| 2025-09-01T06:06:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T06:06:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756706590
|
omerbkts
| 2025-09-01T06:03:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T06:03:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756706555
|
klmdr22
| 2025-09-01T06:03:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T06:03:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samunder12/llama-3.1-8b-roleplay-lora
|
samunder12
| 2025-09-01T06:01:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T06:00:18Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** samunder12
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-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)
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756703841
|
Sayemahsjn
| 2025-09-01T05:36:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T05:36:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756701893
|
acidjp
| 2025-09-01T05:29:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T05:29:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756703641
|
klmdr22
| 2025-09-01T05:14:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T05:14:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CodeAtCMU/Llama-3.2-1B-GenerativePerturbations_full_sft_code_data_120K_step_by_step
|
CodeAtCMU
| 2025-09-01T05:00:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T05:00:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aXsalll/blockassist-bc-chattering_galloping_ape_1756702593
|
aXsalll
| 2025-09-01T04:57:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering galloping ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:56:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering galloping ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756700352
|
koloni
| 2025-09-01T04:44:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:44:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
v2ray/nai-lora-heavy-line
|
v2ray
| 2025-09-01T04:44:57Z | 0 | 0 |
peft
|
[
"peft",
"art",
"text-to-image",
"en",
"base_model:Laxhar/noobai-xl-EarlyAccess",
"base_model:adapter:Laxhar/noobai-xl-EarlyAccess",
"license:mit",
"region:us"
] |
text-to-image
| 2025-08-31T06:01:48Z |
---
license: mit
language:
- en
base_model:
- Laxhar/sdxl_noob
pipeline_tag: text-to-image
tags:
- art
library_name: peft
---
# NoobAI XL LoRA Heavy Line
This LoRA is trained for 2 models, [heavy-line.safetensors](https://huggingface.co/v2ray/nai-lora-heavy-line/resolve/main/heavy-line.safetensors) for [v1.1 version of NoobAI XL](https://civitai.com/models/833294?modelVersionId=1116447), and [heavy-line-mmh.safetensors](https://huggingface.co/v2ray/nai-lora-heavy-line/resolve/main/heavy-line-mmh.safetensors) for [Vpred 1.1 version of MiaoMiao Harem](https://civitai.com/models/934764?modelVersionId=1690053).
The dataset used to train this LoRA is scraped using [LagPixelLOL/aisp](https://github.com/LagPixelLOL/aisp), containing a total of 578 images, a total of 3 artists are used.
Big thanks to the artists for the very cute styles :3.
To use this LoRA, you can go without a trigger word, which will use all 3 artists' style together, or you can choose to specify which artist's style with a trigger word, note this model is mostly a foot model. \
pixiv [@くろやくそく](https://www.pixiv.net/users/6478220): `hei yksk` \
pixiv [@leonzo030](https://www.pixiv.net/users/13765232): `leonzo` \
pixiv [@フリザ](https://www.pixiv.net/users/67904089): `efreezerarts`
This LoRA is trained using [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts), with rank 32, alpha 16, learning rate 1e-4, for 192 epochs with a total of 5184 steps, using a B200, took approximately 6 hours.
If you have any questions, suggestions, or just want to talk to me, you can add me on Discord with ID [@v2ray](https://discord.gg/r4Wj97nZ).
## Examples
|
yujiepan/longcat-flash-tiny-random
|
yujiepan
| 2025-09-01T04:36:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"longcat_flash",
"text-generation",
"conversational",
"custom_code",
"base_model:meituan-longcat/LongCat-Flash-Chat",
"base_model:finetune:meituan-longcat/LongCat-Flash-Chat",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-09-01T04:36:39Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- meituan-longcat/LongCat-Flash-Chat
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [meituan-longcat/LongCat-Flash-Chat](https://huggingface.co/meituan-longcat/LongCat-Flash-Chat).
### Example usage:
- vLLM
```bash
vllm serve yujiepan/longcat-flash-tiny-random \
--trust-remote-code \
--enable-expert-parallel \
--tensor-parallel-size 1 \
--speculative_config '{"model": "yujiepan/longcat-flash-tiny-random", "num_speculative_tokens": 1, "method":"longcat_flash_mtp"}'
```
- SGLang
```bash
python3 -m sglang.launch_server \
--model yujiepan/longcat-flash-tiny-random \
--trust-remote-code \
--attention-backend flashinfer \
--enable-ep-moe \
--tp 1 \
--speculative-draft-model-path yujiepan/longcat-flash-tiny-random \
--speculative-algorithm NEXTN \
--speculative-num-draft-tokens 2 \
--speculative-num-steps 1 \
--speculative-eagle-topk 1
```
- Transformers
```python
import torch
import transformers
model_id = "yujiepan/longcat-flash-tiny-random"
pipe = transformers.pipelines.pipeline(
'text-generation',
model=model_id,
trust_remote_code=True,
device_map='cuda',
torch_dtype=torch.bfloat16,
)
past_key_values = transformers.DynamicCache(config=None) # set config to None
r = pipe('Hello, world!', past_key_values=past_key_values, max_new_tokens=32)
print(r)
```
### Codes to create this repo:
```python
import json
from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
from transformers.models.glm4_moe.modeling_glm4_moe import Glm4MoeRMSNorm
source_model_id = "meituan-longcat/LongCat-Flash-Chat"
save_folder = "/tmp/yujiepan/longcat-flash-tiny-random"
Path(save_folder).mkdir(parents=True, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(source_model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
config_json.update({
'num_layers': 2,
'hidden_size': 8,
'ffn_hidden_size': 64,
'expert_ffn_hidden_size': 64,
'num_attention_heads': 4,
'kv_lora_rank': 384,
'n_routed_experts': 32,
'q_lora_rank': 32,
'qk_nope_head_dim': 64,
'qk_rope_head_dim': 192, # vllm mla kernel supports 576 only, FA supports head dim <= 256
'v_head_dim': 64,
'moe_topk': 12,
'zero_expert_num': 16,
})
# del config_json['quantization_config']
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
model = model.cpu()
# MTP
model.model.mtp = nn.ModuleDict({
"layers": nn.ModuleList([nn.ModuleDict(dict(
eh_proj=nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False),
enorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
hnorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
input_layernorm=nn.RMSNorm(config.hidden_size),
post_attention_layernorm=nn.RMSNorm(config.hidden_size),
self_attn=deepcopy(model.model.layers[0].self_attn[0]),
transformer_layer=nn.ModuleDict({"mlp": deepcopy(model.model.layers[0].mlps[0])}),
))]),
"norm": nn.RMSNorm(config.hidden_size),
})
for i in range(config.num_layers):
model.model.layers[i].mlp.router = model.model.layers[i].mlp.router.float()
# model.model.layers[i].mlp.router.e_score_correction_bias = torch.zeros((config.n_routed_experts + config.zero_expert_num)).float()
set_seed(42)
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape, p.dtype)
model.model.mtp.embed_tokens = deepcopy(model.model.embed_tokens)
model.save_pretrained(save_folder)
torch.set_default_dtype(torch.float32)
for n, m in model.named_modules():
if 'LongcatFlashMLA' in str(type(m)):
print(n, m.layer_idx)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = {k: v.split('--')[-1] for k, v in config_json['auto_map'].items()}
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
```
### Printing the model:
```text
LongcatFlashForCausalLM(
(model): LongcatFlashModel(
(embed_tokens): Embedding(131072, 8)
(layers): ModuleList(
(0-1): 2 x LongcatFlashDecoderLayer(
(mlp): LongcatFlashMoE(
(experts): ModuleList(
(0-31): 32 x LongcatFlashMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
)
(router): LongcatFlashTopkRouter(
(classifier): Linear(in_features=8, out_features=48, bias=False)
)
)
(self_attn): ModuleList(
(0-1): 2 x LongcatFlashMLA(
(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
)
(mlps): ModuleList(
(0-1): 2 x LongcatFlashMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
)
(input_layernorm): ModuleList(
(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
)
(post_attention_layernorm): ModuleList(
(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
)
)
)
(norm): LongcatFlashRMSNorm((8,), eps=1e-05)
(rotary_emb): LongcatFlashRotaryEmbedding()
(mtp): ModuleDict(
(layers): ModuleList(
(0): ModuleDict(
(eh_proj): Linear(in_features=16, out_features=8, bias=False)
(enorm): ModuleDict(
(m): RMSNorm((8,), eps=None, elementwise_affine=True)
)
(hnorm): ModuleDict(
(m): RMSNorm((8,), eps=None, elementwise_affine=True)
)
(input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(self_attn): LongcatFlashMLA(
(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(transformer_layer): ModuleDict(
(mlp): LongcatFlashMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLU()
)
)
)
)
(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
(embed_tokens): Embedding(131072, 8)
)
)
(lm_head): Linear(in_features=8, out_features=131072, bias=False)
)
```
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756698816
|
Loder-S
| 2025-09-01T04:21:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:21:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly knobby tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wjkim9653/llama-3.2-3b-instruct-ldi-clinic-base-rlaif-rlhf
|
wjkim9653
| 2025-09-01T04:19:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T04:11:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756700135
|
liukevin666
| 2025-09-01T04:16:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:16:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-beaked_lumbering_cockroach_1756700128
|
AnerYubo
| 2025-09-01T04:15:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked lumbering cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:15:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked lumbering cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756699837
|
akirafudo
| 2025-09-01T04:10:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T04:10:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samairtimer/gemma-3-270m-it-blr-slang
|
samairtimer
| 2025-09-01T04:08:50Z | 0 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gguf",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:quantized:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T07:28:18Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-blr-slang
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-270m-it-blr-slang
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="samairtimer/gemma-3-270m-it-blr-slang", 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: 0.22.1
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
pjngth998/lora-datasetv02-Llama-3.1-8B-customer-service-chatbot
|
pjngth998
| 2025-09-01T03:59:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-01T03:50:09Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
- 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
AppliedLucent/ALIE-1.2-8B
|
AppliedLucent
| 2025-09-01T03:57:57Z | 44 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:AppliedLucent/ALIE-1.2-8B",
"base_model:finetune:AppliedLucent/ALIE-1.2-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T19:32:48Z |
---
base_model: AppliedLucent/ALIE-1.2-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AppliedLucent
- **License:** apache-2.0
- **Finetuned from model :** AppliedLucent/ALIE-1.2-8B
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)
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756696480
|
rvipitkirubbe
| 2025-09-01T03:41:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:41:00Z |
---
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).
|
ymatari/act_so101_cleanup_table_4
|
ymatari
| 2025-09-01T03:34:57Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:ymatari/cleanup-table-2",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T03:34:27Z |
---
datasets: ymatari/cleanup-table-2
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
sekirr/blockassist-bc-masked_tenacious_whale_1756697640
|
sekirr
| 2025-09-01T03:34:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:34:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756697234
|
akirafudo
| 2025-09-01T03:27:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:27:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756696480
|
2hpsatt
| 2025-09-01T03:15:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:15:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kznmp3/blockassist-bc-lively_raging_hippo_1756695931
|
kznmp3
| 2025-09-01T03:06:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lively raging hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:06:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lively raging hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756695925
|
akirafudo
| 2025-09-01T03:05:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T03:05:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
danuphat/typhoon-ocr-7b-5-down-ep-3
|
danuphat
| 2025-09-01T03:03:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:scb10x/typhoon-ocr-7b",
"base_model:finetune:scb10x/typhoon-ocr-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T02:01:27Z |
---
base_model: scb10x/typhoon-ocr-7b
library_name: transformers
model_name: typhoon-ocr-7b-5-down-ep-3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for typhoon-ocr-7b-5-down-ep-3
This model is a fine-tuned version of [scb10x/typhoon-ocr-7b](https://huggingface.co/scb10x/typhoon-ocr-7b).
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="danuphat/typhoon-ocr-7b-5-down-ep-3", 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/danuphat-l-kasetsart-university/typhoon-ocr-7b-add-data-1/runs/0v5mykn1)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.56.0.dev0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
giovannidemuri/llama8b-er-v519-seed2-hx
|
giovannidemuri
| 2025-09-01T02:54:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T01:11:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756694775
|
akirafudo
| 2025-09-01T02:47:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:46:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756693228
|
GroomerG
| 2025-09-01T02:41:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:41:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756692306
|
NahedDom
| 2025-09-01T02:38:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:38:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# 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_1756692535
|
maxibillion1975
| 2025-09-01T02:36:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:35:57Z |
---
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).
|
kalimoy/blockassist-bc-playful_huge_nightingale_1756693352
|
kalimoy
| 2025-09-01T02:23:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful huge nightingale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:22:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful huge nightingale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kalimoy/blockassist-bc-soft_curious_camel_1756692448
|
kalimoy
| 2025-09-01T02:08:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft curious camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:07:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft curious camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756692165
|
sekirr
| 2025-09-01T02:03:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T02:03:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jiarr/Qwen3-0.6B-Gensyn-Swarm-plump_burrowing_capybara
|
jiarr
| 2025-09-01T02:02:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am plump_burrowing_capybara",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T01:58:30Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am plump_burrowing_capybara
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756691753
|
akirafudo
| 2025-09-01T01:56:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:56:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756690472
|
bah63843
| 2025-09-01T01:35:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:35:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1756689725
|
vendi11
| 2025-09-01T01:22:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:22:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-NEW3_t1_50k_v2_tag5_filtered_hermes
|
lemonhat
| 2025-09-01T01:21:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T01:20:29Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: NEW3_t1_50k_v2_tag5_filtered_hermes
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. -->
# NEW3_t1_50k_v2_tag5_filtered_hermes
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the NEW3_t1_50k_v2_tag5_filtered_hermes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2518 | 0.0628 | 100 | 0.2473 |
| 0.2409 | 0.1255 | 200 | 0.2294 |
| 0.2612 | 0.1883 | 300 | 0.2220 |
| 0.1955 | 0.2511 | 400 | 0.2177 |
| 0.2403 | 0.3139 | 500 | 0.2129 |
| 0.2477 | 0.3766 | 600 | 0.2118 |
| 0.1885 | 0.4394 | 700 | 0.2045 |
| 0.1904 | 0.5022 | 800 | 0.2051 |
| 0.2349 | 0.5650 | 900 | 0.1997 |
| 0.2077 | 0.6277 | 1000 | 0.1944 |
| 0.1978 | 0.6905 | 1100 | 0.1921 |
| 0.21 | 0.7533 | 1200 | 0.1960 |
| 0.2057 | 0.8161 | 1300 | 0.1938 |
| 0.1966 | 0.8788 | 1400 | 0.1910 |
| 0.2953 | 0.9416 | 1500 | 0.1890 |
| 0.1847 | 1.0044 | 1600 | 0.1881 |
| 0.2031 | 1.0672 | 1700 | 0.1892 |
| 0.1982 | 1.1299 | 1800 | 0.1861 |
| 0.1926 | 1.1927 | 1900 | 0.1846 |
| 0.1627 | 1.2555 | 2000 | 0.1835 |
| 0.1849 | 1.3183 | 2100 | 0.1834 |
| 0.2375 | 1.3810 | 2200 | 0.1826 |
| 0.1617 | 1.4438 | 2300 | 0.1827 |
| 0.1851 | 1.5066 | 2400 | 0.1816 |
| 0.2603 | 1.5694 | 2500 | 0.1829 |
| 0.1864 | 1.6321 | 2600 | 0.1824 |
| 0.1699 | 1.6949 | 2700 | 0.1808 |
| 0.1743 | 1.7577 | 2800 | 0.1801 |
| 0.1735 | 1.8205 | 2900 | 0.1801 |
| 0.2142 | 1.8832 | 3000 | 0.1798 |
| 0.1628 | 1.9460 | 3100 | 0.1797 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Andra76/blockassist-bc-deadly_enormous_butterfly_1756688920
|
Andra76
| 2025-09-01T01:19:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly enormous butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:18:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly enormous butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/1661004
|
seraphimzzzz
| 2025-09-01T01:19:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T01:19:15Z |
[View on Civ Archive](https://civarchive.com/models/1555551?modelVersionId=1760268)
|
bah63843/blockassist-bc-plump_fast_antelope_1756689151
|
bah63843
| 2025-09-01T01:13:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:13:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sapie-model/sapie-guarian-fp8
|
sapie-model
| 2025-09-01T01:09:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"vllm",
"vision",
"fp8",
"conversational",
"en",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
image-text-to-text
| 2025-09-01T01:05:42Z |
---
tags:
- vllm
- vision
- fp8
license: apache-2.0
license_link: >-
https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
- en
base_model: google/gemma-3-27b-it
library_name: transformers
---
# gemma-3-27b-it-FP8-Dynamic
## Model Overview
- **Model Architecture:** gemma-3-27b-it
- **Input:** Vision-Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 2/24/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it).
### Model Optimizations
This model was obtained by quantizing the weights of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) to FP8 data type, ready for inference with vLLM >= 0.5.2.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset
from transformers import AutoProcessor
# Define model name once
model_name = "RedHatAI/gemma-3-27b-it-FP8-dynamic"
# Load image and processor
image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
# Build multimodal prompt
chat = [
{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What is the content of this image?"}]},
{"role": "assistant", "content": []}
]
prompt = processor.apply_chat_template(chat, add_generation_prompt=True)
# Initialize model
llm = LLM(model=model_name, trust_remote_code=True)
# Run inference
inputs = {"prompt": prompt, "multi_modal_data": {"image": [image]}}
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
# Display result
print("RESPONSE:", outputs[0].outputs[0].text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.
<details>
<summary>Model Creation Code</summary>
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from llmcompressor.transformers import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
# Load model.
model_id = google/gemma-3-27b-it
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto", torch_dtype="auto"
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
# Recipe
recipe = [
QuantizationModifier(
targets="Linear",
scheme="FP8_DYNAMIC",
sequential_targets=["Gemma3DecoderLayer"],
ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
),
]
SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"
# Perform oneshot
oneshot(
model=model,
recipe=recipe,
trust_remote_code_model=True,
output_dir=SAVE_DIR
)
```
</details>
## Evaluation
The model was evaluated using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for OpenLLM v1 text benchmark. The evaluations were conducted using the following commands:
<details>
<summary>Evaluation Commands</summary>
### OpenLLM v1
```
lm_eval \
--model vllm \
--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True,enforce_eager=True \
--tasks openllm \
--batch_size auto
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>google/gemma-3-27b-it</th>
<th>RedHatAI/gemma-3-27b-it-FP8-Dynamic</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC Challenge</td>
<td>72.53%</td>
<td>72.70%</td>
<td>100.24%</td>
</tr>
<tr>
<td>GSM8K</td>
<td>92.12%</td>
<td>91.51%</td>
<td>99.34%</td>
</tr>
<tr>
<td>Hellaswag</td>
<td>85.78%</td>
<td>85.69%</td>
<td>99.90%</td>
</tr>
<tr>
<td>MMLU</td>
<td>77.53%</td>
<td>77.45%</td>
<td>99.89%</td>
</tr>
<tr>
<td>Truthfulqa (mc2)</td>
<td>62.20%</td>
<td>62.20%</td>
<td>99.99%</td>
</tr>
<tr>
<td>Winogrande</td>
<td>79.40%</td>
<td>78.77%</td>
<td>99.20%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>78.26%</b></td>
<td><b>78.05%</b></td>
<td><b>99.73%</b></td>
</tr>
<tr>
<td rowspan="3"><b>Vision Evals</b></td>
<td>MMMU (val)</td>
<td>50.89%</td>
<td>51.00%</td>
<td>100.22%</td>
</tr>
<tr>
<td>ChartQA</td>
<td>72.16%</td>
<td>72.16%</td>
<td>100.0%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>61.53%</b></td>
<td><b>61.58%</b></td>
<td><b>100.11%%</b></td>
</tr>
</tbody>
</table>
|
bah63843/blockassist-bc-plump_fast_antelope_1756688772
|
bah63843
| 2025-09-01T01:07:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:06:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756688431
|
bah63843
| 2025-09-01T01:01:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T01:01:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phamff/vietnamese-legal-lora-adapter
|
phamff
| 2025-09-01T00:57:06Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"vietnamese",
"legal",
"qa",
"lora",
"vi",
"base_model:1TuanPham/T-VisStar-7B-v0.1",
"base_model:adapter:1TuanPham/T-VisStar-7B-v0.1",
"region:us"
] | null | 2025-09-01T00:56:38Z |
---
library_name: peft
base_model: 1TuanPham/T-VisStar-7B-v0.1
tags:
- vietnamese
- legal
- qa
- lora
language: vi
---
# Vietnamese Legal QA LoRA Adapter
LoRA adapter for Vietnamese legal Q&A, trained on `1TuanPham/T-VisStar-7B-v0.1`.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"1TuanPham/T-VisStar-7B-v0.1",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "phamff/vietnamese-legal-lora-adapter")
tokenizer = AutoTokenizer.from_pretrained("1TuanPham/T-VisStar-7B-v0.1")
# Generate
question = "Quyền và nghĩa vụ của công dân là gì?"
prompt = f"<|user|>\n{question}\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer.split("<|assistant|>\n")[-1])
```
Trained: 2025-09-01
|
haider-shah-viral-videos-35-second-Video/New.full.videos.haider.shah.Viral.Video.Official.Tutorial
|
haider-shah-viral-videos-35-second-Video
| 2025-09-01T00:56:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T00:56:03Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" 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>
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756684748
|
elmenbillion
| 2025-09-01T00:26:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T00:25:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/llama3-diverce-ver1.6-i1-GGUF
|
mradermacher
| 2025-09-01T00:21:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:sel303/llama3-diverce-ver1.6",
"base_model:quantized:sel303/llama3-diverce-ver1.6",
"license:llama3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-31T23:40:30Z |
---
base_model: sel303/llama3-diverce-ver1.6
language:
- en
library_name: transformers
license: llama3
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/sel303/llama3-diverce-ver1.6
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#llama3-diverce-ver1.6-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/llama3-diverce-ver1.6-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/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-diverce-ver1.6-i1-GGUF/resolve/main/llama3-diverce-ver1.6.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756685260
|
liukevin666
| 2025-09-01T00:09:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T00:08:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
barchimnases/blockassist-bc-sedate_masked_spider_1756684223
|
barchimnases
| 2025-08-31T23:51:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sedate masked spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T23:50:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sedate masked spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
golopper/blockassist-bc-sneaky_howling_eagle_1756681538
|
golopper
| 2025-08-31T23:06:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sneaky howling eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T23:05:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sneaky howling eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jouuer/blockassist-bc-eager_fast_vulture_1756681377
|
jouuer
| 2025-08-31T23:03:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"eager fast vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T23:02:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- eager fast vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
golopper/blockassist-bc-savage_pale_rhino_1756680978
|
golopper
| 2025-08-31T22:56:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage pale rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T22:56:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage pale rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ypszn/blockassist-bc-yapping_pawing_worm_1756680929
|
ypszn
| 2025-08-31T22:56:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T22:56:16Z |
---
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).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756680640
|
Vasya777
| 2025-08-31T22:51:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T22:51:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756678904
|
elmenbillion
| 2025-08-31T22:47:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T22:47:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# 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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