modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756000083
|
kojeklollipop
| 2025-08-24T02:14:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:13:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1756000066
|
thanobidex
| 2025-08-24T02:12:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:12:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756000908
|
eshanroy5678
| 2025-08-24T02:08:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:05:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hongnhungnguyenthi1088/blockassist-bc-hoarse_whiskered_gerbil_1756000435
|
hongnhungnguyenthi1088
| 2025-08-24T02:07:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hoarse whiskered gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:07:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hoarse whiskered gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/PsycoLLM-GGUF
|
mradermacher
| 2025-08-24T02:07:00Z | 65 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:MindIntLab/PsycoLLM",
"base_model:quantized:MindIntLab/PsycoLLM",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-05T07:24:53Z |
---
base_model: MindIntLab/PsycoLLM
language:
- en
library_name: transformers
license: other
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- llama-factory
- full
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/MindIntLab/PsycoLLM
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PsycoLLM-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q2_K.gguf) | Q2_K | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q3_K_S.gguf) | Q3_K_S | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q3_K_L.gguf) | Q3_K_L | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.IQ4_XS.gguf) | IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q4_K_M.gguf) | Q4_K_M | 9.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q5_K_S.gguf) | Q5_K_S | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PsycoLLM-GGUF/resolve/main/PsycoLLM.Q8_0.gguf) | Q8_0 | 15.2 | 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 -->
|
Septian1/blockassist-bc-barky_ferocious_bear_1756000998
|
Septian1
| 2025-08-24T02:05:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"barky ferocious bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:05:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- barky ferocious bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755999458
|
coelacanthxyz
| 2025-08-24T02:04:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:04:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/gpt-oss-20b-qx5-mlx
|
nightmedia
| 2025-08-24T02:02:41Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"5-bit",
"region:us"
] |
text-generation
| 2025-08-24T00:56:42Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-20b
---
# gpt-oss-20b-qx5-mlx
This model [gpt-oss-20b-qx5-mlx](https://huggingface.co/gpt-oss-20b-qx5-mlx) was
converted to MLX format from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("gpt-oss-20b-qx5-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755999208
|
katanyasekolah
| 2025-08-24T02:01:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T02:01:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-18-Anjali-Arora-viral-Video-Clip/New.full.videos.Anjali.Arora.Viral.Video.Official.Tutorial
|
VIDEOS-18-Anjali-Arora-viral-Video-Clip
| 2025-08-24T01:59:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T01:59:31Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
hZzy/mistral-7b-expo-7b-IPO-25-08-try-2
|
hZzy
| 2025-08-24T01:59:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"expo",
"trl",
"arxiv:2305.18290",
"base_model:hZzy/mistral-7b-sft-25-1",
"base_model:finetune:hZzy/mistral-7b-sft-25-1",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T17:49:29Z |
---
base_model: hZzy/mistral-7b-sft-25-1
library_name: transformers
model_name: mistral-7b-expo-7b-IPO-25-08-try-2
tags:
- generated_from_trainer
- expo
- trl
licence: license
---
# Model Card for mistral-7b-expo-7b-IPO-25-08-try-2
This model is a fine-tuned version of [hZzy/mistral-7b-sft-25-1](https://huggingface.co/hZzy/mistral-7b-sft-25-1).
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="hZzy/mistral-7b-expo-7b-IPO-25-08-try-2", 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/zhiyuzha-university-of-florida/huggingface/runs/kh0vrxq0)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.20.0
- Transformers: 4.54.1
- Pytorch: 2.7.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mahmoudOmar03/writing_task22_scores_only
|
mahmoudOmar03
| 2025-08-24T01:59:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T01:59:40Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mahmoudOmar03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 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)
|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1756000307
|
eshanroy5678
| 2025-08-24T01:58:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:56:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kronu/gemma-2-2b-lean-expert-1760-complete
|
Kronu
| 2025-08-24T01:57:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2-2b",
"lora",
"transformers",
"text-generation",
"base_model:google/gemma-2-2b",
"license:gemma",
"region:us"
] |
text-generation
| 2025-08-24T01:03:31Z |
---
library_name: peft
license: gemma
base_model: google/gemma-2-2b
tags:
- base_model:adapter:google/gemma-2-2b
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: gemma-2-2b-lean-expert-1760-complete
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. -->
# gemma-2-2b-lean-expert-1760-complete
This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.1
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
mradermacher/PyroNet-GGUF
|
mradermacher
| 2025-08-24T01:57:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"ru",
"uk",
"base_model:Kenan023214/PyroNet",
"base_model:quantized:Kenan023214/PyroNet",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-23T18:33:34Z |
---
base_model: Kenan023214/PyroNet
language:
- en
- ru
- uk
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Kenan023214/PyroNet
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PyroNet-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/PyroNet-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/PyroNet-GGUF/resolve/main/PyroNet.Q3_K_S.gguf) | Q3_K_S | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q2_K.gguf) | Q2_K | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.IQ4_XS.gguf) | IQ4_XS | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q3_K_M.gguf) | Q3_K_M | 13.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q3_K_L.gguf) | Q3_K_L | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q4_K_S.gguf) | Q4_K_S | 14.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q4_K_M.gguf) | Q4_K_M | 15.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q5_K_S.gguf) | Q5_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q5_K_M.gguf) | Q5_K_M | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-GGUF/resolve/main/PyroNet.Q8_0.gguf) | Q8_0 | 22.4 | 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 -->
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755998929
|
mang3dd
| 2025-08-24T01:54:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:54:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755998858
|
vwzyrraz7l
| 2025-08-24T01:53:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:53:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755998871
|
ihsanridzi
| 2025-08-24T01:53:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:53:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nguyenhungtuan1087/blockassist-bc-winged_bold_butterfly_1755999392
|
nguyenhungtuan1087
| 2025-08-24T01:49:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged bold butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:49:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged bold butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
marcelone/Jinx-Qwen3-32B-gguf
|
marcelone
| 2025-08-24T01:48:57Z | 128 | 0 | null |
[
"gguf",
"base_model:Jinx-org/Jinx-Qwen3-32B",
"base_model:quantized:Jinx-org/Jinx-Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-15T14:44:40Z |
---
license: apache-2.0
base_model: Jinx-org/Jinx-Qwen3-32B
base_model_relation: quantized
---
|
Orginal-Chitra-Tripathi-Viral-Video-Clip/New.full.videos.Chitra.Tripathi.Viral.Video.Official.Tutorial
|
Orginal-Chitra-Tripathi-Viral-Video-Clip
| 2025-08-24T01:48:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T01:48:38Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755998549
|
calegpedia
| 2025-08-24T01:47:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:47:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# 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_1755998359
|
elmenbillion
| 2025-08-24T01:46:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:46:02Z |
---
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).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755998421
|
manusiaperahu2012
| 2025-08-24T01:45:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:45:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ysramen/TwinLlama-3.1-8B
|
ysramen
| 2025-08-24T01:42:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.1-8B",
"base_model:finetune:unsloth/Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T01:33:37Z |
---
base_model: unsloth/Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ysramen
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.1-8B
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)
|
unitova/blockassist-bc-zealous_sneaky_raven_1755997962
|
unitova
| 2025-08-24T01:40:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:40:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
andrewmonostate/monostate-model-8df4699e
|
andrewmonostate
| 2025-08-24T01:40:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"text-generation",
"fine-tuned",
"monostate",
"conversational",
"en",
"dataset:custom",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-24T01:40:13Z |
---
license: apache-2.0
base_model: unsloth/gemma-3-270m-it
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- monostate
datasets:
- custom
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# monostate-model-8df4699e
This model is a fine-tuned version of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it).
## Model Description
This model was fine-tuned using the Monostate training platform with LoRA (Low-Rank Adaptation) for efficient training.
## Training Details
### Training Data
- Dataset size: 162 samples
- Training type: Supervised Fine-Tuning (SFT)
### Training Procedure
#### Training Hyperparameters
- Training regime: Mixed precision (fp16)
- Optimizer: AdamW
- LoRA rank: 128
- LoRA alpha: 128
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
#### Training Results
- Final loss: 1.1047305989265441
- Training time: 0.6 minutes
- Generated on: 2025-08-23T18:40:13.555097
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("andrewmonostate/monostate-model-8df4699e")
tokenizer = AutoTokenizer.from_pretrained("andrewmonostate/monostate-model-8df4699e")
# Generate text
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.7,
do_sample=True,
top_p=0.95,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Framework Versions
- Transformers: 4.40+
- PyTorch: 2.0+
- Datasets: 2.0+
- Tokenizers: 0.19+
## License
This model is licensed under the Apache 2.0 License.
## Citation
If you use this model, please cite:
```bibtex
@misc{andrewmonostate_monostate_model_8df4699e,
title={monostate-model-8df4699e},
author={Monostate},
year={2024},
publisher={HuggingFace},
url={https://huggingface.co/andrewmonostate/monostate-model-8df4699e}
}
```
## Training Platform
This model was trained using [Monostate](https://monostate.ai), an AI training and deployment platform.
|
hobaratio/MN-Violet-Lotus-12B-mlx-4Bit
|
hobaratio
| 2025-08-24T01:39:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"storywriting",
"text adventure",
"creative",
"story",
"writing",
"fiction",
"roleplaying",
"rp",
"mergekit",
"merge",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:FallenMerick/MN-Violet-Lotus-12B",
"base_model:quantized:FallenMerick/MN-Violet-Lotus-12B",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-24T01:39:13Z |
---
license: cc-by-4.0
language:
- en
base_model: FallenMerick/MN-Violet-Lotus-12B
library_name: transformers
tags:
- storywriting
- text adventure
- creative
- story
- writing
- fiction
- roleplaying
- rp
- mergekit
- merge
- mlx
- mlx-my-repo
---
# hobaratio/MN-Violet-Lotus-12B-mlx-4Bit
The Model [hobaratio/MN-Violet-Lotus-12B-mlx-4Bit](https://huggingface.co/hobaratio/MN-Violet-Lotus-12B-mlx-4Bit) was converted to MLX format from [FallenMerick/MN-Violet-Lotus-12B](https://huggingface.co/FallenMerick/MN-Violet-Lotus-12B) using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("hobaratio/MN-Violet-Lotus-12B-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
eshanroy5678/blockassist-bc-untamed_dextrous_dingo_1755999222
|
eshanroy5678
| 2025-08-24T01:39:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed dextrous dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:38:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed dextrous dingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755998019
|
thanobidex
| 2025-08-24T01:39:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:39:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-18-Nisha-Guragain-Viral-Video-Clip/New.full.videos.Nisha.Guragain.Viral.Video.Official.Tutorial
|
VIDEOS-18-Nisha-Guragain-Viral-Video-Clip
| 2025-08-24T01:37:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T01:36:47Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
hobaratio/MN-Violet-Lotus-12B-mlx-8Bit
|
hobaratio
| 2025-08-24T01:36:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"storywriting",
"text adventure",
"creative",
"story",
"writing",
"fiction",
"roleplaying",
"rp",
"mergekit",
"merge",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:FallenMerick/MN-Violet-Lotus-12B",
"base_model:quantized:FallenMerick/MN-Violet-Lotus-12B",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-24T01:35:52Z |
---
license: cc-by-4.0
language:
- en
base_model: FallenMerick/MN-Violet-Lotus-12B
library_name: transformers
tags:
- storywriting
- text adventure
- creative
- story
- writing
- fiction
- roleplaying
- rp
- mergekit
- merge
- mlx
- mlx-my-repo
---
# hobaratio/MN-Violet-Lotus-12B-mlx-8Bit
The Model [hobaratio/MN-Violet-Lotus-12B-mlx-8Bit](https://huggingface.co/hobaratio/MN-Violet-Lotus-12B-mlx-8Bit) was converted to MLX format from [FallenMerick/MN-Violet-Lotus-12B](https://huggingface.co/FallenMerick/MN-Violet-Lotus-12B) using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("hobaratio/MN-Violet-Lotus-12B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755997762
|
indoempatnol
| 2025-08-24T01:36:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:36:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755997816
|
rafsya427
| 2025-08-24T01:36:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:36:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karamoka/gensyn
|
karamoka
| 2025-08-24T01:36:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T00:41:29Z |
# BlockAssist
<div align="center">
</div>
**BlockAssist** is an AI assistant that learns from its user’s actions in Minecraft. The assistant appears in-game with you, starting with only basic knowledge of the game’s commands. As you play, it learns how to assist you in building, learning directly from your actions. It shows an early demo of _assistance learning_ - a new paradigm for aligning agents to human preferences across domains.
Steps:
1. Follow setup instructions below
2. Play Minecraft episodes and complete the building goal in the shortest time possible. This will help train the best assistant models.
3. Share your progress with the community by posting your gameplay videos, stats, and Hugging Face uploads on Discord and X. Track your participation on the leaderboard.
**You do not need a copy of Minecraft to play! BlockAssist includes a free version.**
## Installation (macOS)
*You only need to run these once per computer.*
**Step 1: Clone the repo and enter the directory**
```bash
git clone https://github.com/gensyn-ai/blockassist.git
cd blockassist
```
**Step 2: Install Java 1.8.0_152**
Run the setup script:
```bash
./setup.sh
```
**Step 3: Install `pyenv`**
**Note**: This step assumes [Homebrew](https://brew.sh/) is installed on your Mac
```bash
brew update
brew install pyenv
```
**Step 4: Install Python 3.10**
```bash
pyenv install 3.10
```
**Step 5: Install `psutil` and `readchar`**
```bash
pyenv exec pip install psutil readchar
```
## Installation (Linux)
*You only need to run these once per computer.*
**Step 1: Clone the repo and enter the directory**
```bash
git clone https://github.com/gensyn-ai/blockassist.git
cd blockassist
```
**Step 2: Install Java 1.8.0_152**
Run the setup script:
```bash
./setup.sh
```
**Step 3: Install `pyenv`**
```bash
curl -fsSL https://pyenv.run | bash
```
**Note:** Follow the instructions `pyenv` prints about adding it to your shell and restart your terminal.
**Step 4: Install Python 3.10**
```bash
sudo apt update
sudo apt install make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev curl git libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev # Dependencies for Python installation
pyenv install 3.10
```
**Step 5: Install `psutil` and `readchar`**
```bash
pip install psutil readchar
```
## Run BlockAssist
Use `ls logs` to list available log files, and `tail -f logs/<name>.log` to monitor progress.
**Note:** when asked to press `ENTER`, you may need to do so a couple of times.
**Run with Python**
* On macOS: `pyenv exec python run.py`
* On Linux: `python run.py`
The program will install additional dependencies as required. Follow any prompts and approve any requests.
**Hugging Face Token**
You will be asked to enter a [Hugging Face](https://huggingface.co) API token. Follow [these instructions](https://huggingface.co/docs/hub/en/security-tokens) to generate one with **Write** access.
**Gensyn Testnet login**
You will be prompted to log in through your browser (`http://localhost:3000`). If you have previously logged in, this step will be skipped. Otherwise, use the browser window that opens to log in.
**Play Minecraft**
Once the Minecraft windows have loaded, the Python script will ask you to press `ENTER`.
Go to the first Minecraft window that opened (the other will be minimized on macOS). Click the window and press `ENTER` to allow it to capture your inputs. Complete the structure in-game, then return to your terminal and press `ENTER` to end the session.
**Training**
A model will now be trained and submitted to Hugging Face and to Gensyn’s smart contract.
**Review logs**
If you reach this stage in the logging window and can see a transaction in the block explorer, your submission has succeeded.
Logging window:
```
[2025-07-28 05:03:48,955][blockassist.globals][INFO] - Successfully uploaded model to HuggingFace: h-grieve/blockassist-bc-bellowing_pouncing_horse_1753675374 with size 20.00 MB
```
[Block explorer](https://gensyn-testnet.explorer.alchemy.com/address/0xE2070109A0C1e8561274E59F024301a19581d45c?tab=logs):
```
huggingFaceID
string
false
<HF-username>/blockassist-bc-bellowing_pouncing_horse_1753675374
```
The program will then end. Please close any Minecraft windows if they remain open.
## Configuration
BlockAssist uses [Hydra](https://github.com/facebookresearch/hydra) for configuration management. You can modify settings in the `config.yaml` file or override them via command-line arguments.
- `episode_count` — Controls the number of episodes. If `episode_count` is greater than 1, a new episode will start each time you press `ENTER` during session recording.
- `num_training_iters` — Controls the number of training iterations across all recorded episodes.
## Testing & Contributing
### Linting / Testing
This project relies on Ruff for formatting/linting. To format imports, run:
```bash
ruff check --select I --fix .
```
## Telemetry
This repository uploads telemetry to Gensyn services. To disable telemetry, export:
```bash
export DISABLE_TELEMETRY=1
```
**Note**: If you turn off telemetry, your contributions may not be counted towards the [BlockAssist leaderboard](https://dashboard.gensyn.ai).
|
tzwilliam0/dpo_Argilla_Math
|
tzwilliam0
| 2025-08-24T01:35:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T01:34:50Z |
---
base_model: unsloth/Qwen3-4B-Base
library_name: transformers
model_name: dpo_Argilla_Math
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for dpo_Argilla_Math
This model is a fine-tuned version of [unsloth/Qwen3-4B-Base](https://huggingface.co/unsloth/Qwen3-4B-Base).
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="tzwilliam0/dpo_Argilla_Math", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
haihp02/aa33b8cf-080d-4ddc-b880-c9a78b6f314c
|
haihp02
| 2025-08-24T01:34:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T01:34:12Z |
---
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]
|
abirnahid85/blockassist-bc-shaggy_bellowing_weasel_1755999097
|
abirnahid85
| 2025-08-24T01:33:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy bellowing weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:33:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy bellowing weasel
---
# 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_1755997915
|
Sayemahsjn
| 2025-08-24T01:30:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:30:09Z |
---
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).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755997424
|
pempekmangedd
| 2025-08-24T01:28:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:28:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
longhoang2112/whisper-turbo-fine-tuning_2_stages_with_covoi
|
longhoang2112
| 2025-08-24T01:28:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"whisper",
"trl",
"en",
"base_model:unsloth/whisper-large-v3-turbo",
"base_model:finetune:unsloth/whisper-large-v3-turbo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T01:28:02Z |
---
base_model: unsloth/whisper-large-v3-turbo
tags:
- text-generation-inference
- transformers
- unsloth
- whisper
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** longhoang2112
- **License:** apache-2.0
- **Finetuned from model :** unsloth/whisper-large-v3-turbo
This whisper 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)
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755997162
|
katanyasekolah
| 2025-08-24T01:27:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:27:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lautan/blockassist-bc-gentle_patterned_goat_1755997268
|
lautan
| 2025-08-24T01:26:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:26:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755996860
|
quantumxnode
| 2025-08-24T01:21:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:21:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kasbon/blockassist-bc-pawing_shy_squirrel_1755998438
|
kasbon
| 2025-08-24T01:21:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing shy squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:21:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing shy squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
apriasmoro/472fc468-7ffb-481a-832e-2b54f6c9fdce
|
apriasmoro
| 2025-08-24T01:21:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:jingyeom/seal3.1.6n_7b",
"base_model:adapter:jingyeom/seal3.1.6n_7b",
"region:us"
] | null | 2025-08-23T14:51:34Z |
---
base_model: jingyeom/seal3.1.6n_7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
Orginal-18-Prerona-viral-video-links/New.full.videos.Prerona.viral.video.Official.Tutorial
|
Orginal-18-Prerona-viral-video-links
| 2025-08-24T01:19:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T01:19:29Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
typkasm5/blockassist-bc-sprightly_durable_locust_1755998253
|
typkasm5
| 2025-08-24T01:18:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly durable locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:17:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly durable locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
John6666/prefect-pony-xl-v6-sdxl
|
John6666
| 2025-08-24T01:17:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"animagine",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-24T01:13:03Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- animagine
- pony
---
Original model is [here](https://civitai.com/models/439889/prefect-pony-xl?modelVersionId=2114187).
This model created by [Goofy_Ai](https://civitai.com/user/Goofy_Ai).
|
apriasmoro/2e0c85b4-2aea-47a1-b33a-b14221f12afe
|
apriasmoro
| 2025-08-24T01:13:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"region:us"
] | null | 2025-08-23T23:51:50Z |
---
base_model: unsloth/llama-3-8b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
aleebaster/blockassist-bc-sly_eager_boar_1755996353
|
aleebaster
| 2025-08-24T01:12:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:12:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Soughing/tpa_xl
|
Soughing
| 2025-08-24T01:12:24Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-01T17:49:13Z |
---
license: apache-2.0
---
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755996377
|
manusiaperahu2012
| 2025-08-24T01:12:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:12:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bakugo123/sft-llama3.1-8b-instruct-device-zero-with-ocr-qa
|
Bakugo123
| 2025-08-24T01:12:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-23T13:27:17Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: transformers
model_name: sft-llama3.1-8b-instruct-device-zero-with-ocr-qa
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sft-llama3.1-8b-instruct-device-zero-with-ocr-qa
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Bakugo123/sft-llama3.1-8b-instruct-device-zero-with-ocr-qa", 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/axiler/sft-llama3.1-8b-instruct-device-zero-with-ocr-qa/runs/0swhyasf)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 2.16.0
- 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}}
}
```
|
aatituanav/roberta-base-bne-mldoc-4cat
|
aatituanav
| 2025-08-24T01:12:12Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"region:us"
] | null | 2025-08-24T01:11:55Z |
# roberta_base_bne_finetuned_mldoc
Modelo TF-Keras + RoBERTa.
|
typkasm5/blockassist-bc-sprightly_durable_locust_1755997851
|
typkasm5
| 2025-08-24T01:11:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly durable locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:11:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly durable locust
---
# 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_1755996218
|
maxibillion1975
| 2025-08-24T01:11:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:11:11Z |
---
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).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755996302
|
lisaozill03
| 2025-08-24T01:09:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:09:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abirnahid85/blockassist-bc-shaggy_bellowing_weasel_1755997673
|
abirnahid85
| 2025-08-24T01:09:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy bellowing weasel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:09:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy bellowing weasel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755996125
|
kojeklollipop
| 2025-08-24T01:09:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:09:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755996129
|
thanobidex
| 2025-08-24T01:07:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:07:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755995895
|
unitova
| 2025-08-24T01:07:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:06:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
V-I-D-E-O-S-18-Maya-G-viral-Video-Clip-XX/New.full.videos.Maya.G.Viral.Video.Official.Tutorial
|
V-I-D-E-O-S-18-Maya-G-viral-Video-Clip-XX
| 2025-08-24T01:06:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T01:06:30Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
outlookAi/BpWkbMXc74
|
outlookAi
| 2025-08-24T01:03:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-24T00:46:32Z |
---
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: charatman
---
# Bpwkbmxc74
<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 `charatman` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "charatman",
"lora_weights": "https://huggingface.co/outlookAi/BpWkbMXc74/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/BpWkbMXc74', weight_name='lora.safetensors')
image = pipeline('charatman').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/BpWkbMXc74/discussions) to add images that show off what you’ve made with this LoRA.
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755995753
|
rafsya427
| 2025-08-24T01:02:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T01:02:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Autoregressive-1.5B-GGUF
|
mradermacher
| 2025-08-24T01:00:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:InfiniAILab/Autoregressive-1.5B",
"base_model:quantized:InfiniAILab/Autoregressive-1.5B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-24T00:29:02Z |
---
base_model: InfiniAILab/Autoregressive-1.5B
language:
- en
library_name: transformers
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/InfiniAILab/Autoregressive-1.5B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Autoregressive-1.5B-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Autoregressive-1.5B-GGUF/resolve/main/Autoregressive-1.5B.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
typkasm5/blockassist-bc-sprightly_durable_locust_1755997154
|
typkasm5
| 2025-08-24T00:59:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly durable locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:59:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly durable locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mikadat/blockassist-bc-gregarious_ferocious_aardvark_1755996928
|
mikadat
| 2025-08-24T00:56:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious ferocious aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:55:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious ferocious aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
18-Orginal-Jhoselyn-Maura-viral-video-link/New.full.videos.Jhoselyn.Maura.Viral.Video.Official.Tutorial
|
18-Orginal-Jhoselyn-Maura-viral-video-link
| 2025-08-24T00:54:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T00:53:56Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3ckkv2u7?Viral-Video-Original-Link" 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>
|
mikadat/blockassist-bc-gregarious_ferocious_aardvark_1755996757
|
mikadat
| 2025-08-24T00:53:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious ferocious aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:52:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious ferocious aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
uppal-farm-girl-original-viral-video-mms/New.full.videos.uppal.farm.girl.Viral.Video.Official.Tutorial
|
uppal-farm-girl-original-viral-video-mms
| 2025-08-24T00:52:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-24T00:52:08Z |
<animated-image data-catalyst=""><a href="https://fubotv24.com/Leaked/?v=video" 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>
|
John6666/cat-pony-real-il-v20-sdxl
|
John6666
| 2025-08-24T00:51:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"asian",
"Chinese",
"BDSM",
"pony",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-24T00:46:50Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- asian
- Chinese
- BDSM
- pony
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/594973/catpony?modelVersionId=2140837).
This model created by [ggyydream](https://civitai.com/user/ggyydream).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755994957
|
ihsanridzi
| 2025-08-24T00:48:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:48:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755994857
|
quantumxnode
| 2025-08-24T00:46:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:46:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sugoitoolkit/Sugoi-14B-Ultra
|
sugoitoolkit
| 2025-08-24T00:43:10Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"translation",
"ja",
"en",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-08-22T00:42:02Z |
---
license: apache-2.0
language:
- ja
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
tags:
- translation
- transformers
---
# Sugoi LLM 14B Ultra (HF version)
Unleashing the full potential of the previous sugoi 14B model, **Sugoi 14B Ultra** delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of **21.38 vs 13.67**. Its prompt-following skills rival those of Qwen 2.5 Base, especially when handling the bracket-heavy text commonly found in RPG Maker projects.
---
## Model Overview
- **Key Improvements**
* Nearly 2× BLEU score boost over previous quantized version (21.38 vs 13.67).
* Stronger prompt adherence, especially with RPGM-style bracketed text.
- **Ideal Use Cases**
* Japanese → English translation—especially for game dialogue or RPG text.
* Interactive environments—works well with chat UIs like LM Studio.
---
## System Prompt & Settings
Must include a system prompt for best performance:
> You are a professional localizer whose primary goal is to translate Japanese to English. You should use colloquial or slang or nsfw vocabulary if it makes the translation more accurate. Always respond in English.
Additional recommendations:
- Context length: ~10 lines (too much may degrade quality).
- In LM Studio, you can interactively ask grammar or context questions, or switch target language via the prompt (quality may vary).
---
## Experimental Features
These features are experimental and may need tuning:
1. **Tool Integration & JSON Output**
2. **RPGM Tag Preservation**
---
## Recommended Sampling Parameters
| Parameter | Value |
|-----------------|--------|
| Temperature | 0.1 |
| Top-K | 40 |
| Top-P | 0.95 |
| Min-P | 0.05 |
| Repeat Penalty | 1.1 |
---
## Evaluation & Comparison
- **Quantitative**: BLEU score doubled vs prior version (21.38 vs 13.67).
- **Qualitative**: Effective with prompt complexity and RPG Maker markup—delivers clean and accurate translations.
---
## Limitations & Usage Notes
- Overly long context may **“poison”** the output—keep it around 10 lines for best results.
- Experimental features like JSON formatting and tag preservation may not always work perfectly—review outputs carefully.
- Performance may vary depending on the prompt complexity and UI/tool environment.
- Only uncensored for translation task with translation system prompt, other use case such as roleplay,chat may still trigger qwen censoring.
---
## Getting the Model
Available via Files and Versions tab above.
|
ypszn/blockassist-bc-yapping_pawing_worm_1755995917
|
ypszn
| 2025-08-24T00:39:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:39:22Z |
---
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).
|
dgambettaphd/M_llm3_run2_gen9_WXS_doc1000_synt64_lr1e-04_acm_FRESH
|
dgambettaphd
| 2025-08-24T00:39:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-24T00:38:56Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
longhoang2112/whisper-turbo-fine-tuning_2_stages_with_vivos
|
longhoang2112
| 2025-08-24T00:36:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"whisper",
"trl",
"en",
"base_model:unsloth/whisper-large-v3-turbo",
"base_model:finetune:unsloth/whisper-large-v3-turbo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-21T08:31:55Z |
---
base_model: unsloth/whisper-large-v3-turbo
tags:
- text-generation-inference
- transformers
- unsloth
- whisper
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** longhoang2112
- **License:** apache-2.0
- **Finetuned from model :** unsloth/whisper-large-v3-turbo
This whisper 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)
|
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1755995706
|
zenqqq
| 2025-08-24T00:36:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless reptilian caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:36:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless reptilian caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755994120
|
rvipitkirubbe
| 2025-08-24T00:36:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:36:24Z |
---
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).
|
ypszn/blockassist-bc-yapping_pawing_worm_1755995536
|
ypszn
| 2025-08-24T00:33:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping pawing worm",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:33:46Z |
---
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).
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1755993844
|
fujiantiiazhraa
| 2025-08-24T00:32:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:31:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JingzeShi/OpenSeek-1.4B-A0.4B
|
JingzeShi
| 2025-08-24T00:31:47Z | 378 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-03T03:28:29Z |
---
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]
|
unitova/blockassist-bc-zealous_sneaky_raven_1755993871
|
unitova
| 2025-08-24T00:31:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:31:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755993802
|
indoempatnol
| 2025-08-24T00:29:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:29:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Nyarin-4B-i1-GGUF
|
mradermacher
| 2025-08-24T00:29:08Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"zh",
"en",
"dataset:liumindmind/NekoQA-10K",
"base_model:Kasugan0/Nyarin-4B",
"base_model:quantized:Kasugan0/Nyarin-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-23T23:32:38Z |
---
base_model: Kasugan0/Nyarin-4B
datasets:
- liumindmind/NekoQA-10K
language:
- zh
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: 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/Kasugan0/Nyarin-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Nyarin-4B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Nyarin-4B-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/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Nyarin-4B-i1-GGUF/resolve/main/Nyarin-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.7 | 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 -->
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755993697
|
rafsya427
| 2025-08-24T00:27:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:27:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
malikka/blockassist-bc-dense_toothy_baboon_1755995152
|
malikka
| 2025-08-24T00:26:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dense toothy baboon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:26:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dense toothy baboon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
urewstok223/blockassist-bc-squeaky_territorial_stingray_1755995016
|
urewstok223
| 2025-08-24T00:24:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squeaky territorial stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:23:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squeaky territorial stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Badnyal/khasi-english-embeddings
|
Badnyal
| 2025-08-24T00:22:31Z | 0 | 0 |
fasttext
|
[
"fasttext",
"embeddings",
"word-embeddings",
"khasi",
"multilingual",
"northeast-india",
"low-resource",
"Meghalaya",
"en",
"kha",
"dataset:custom",
"license:mit",
"model-index",
"region:us"
] | null | 2025-08-23T23:37:25Z |
---
language:
- en
- kha
license: mit
library_name: fasttext
tags:
- embeddings
- word-embeddings
- khasi
- multilingual
- northeast-india
- low-resource
- Meghalaya
datasets:
- custom
metrics:
- cosine_similarity
model-index:
- name: Badnyal/khasi-english-embeddings
results:
- task:
type: word-similarity
name: Cross-lingual Word Similarity
dataset:
name: Khasi-English Parallel Corpus
type: custom
metrics:
- type: cosine_similarity
value: 0.29
name: Cross-lingual Similarity Score
---
# Khasi-English Word Embeddings
## Model Description
This model provides the first comprehensive word embeddings for the Khasi language, trained on a bilingual Khasi-English corpus. Khasi is an Austroasiatic language of the Mon-Khmer branch, spoken primarily in Meghalaya, Northeast India.
## Model Architecture
- **Model Type**: FastText (Skip-gram)
- **Embedding Dimension**: 300
- **Vocabulary Size**: 38,220 tokens
- **Training Algorithm**: Hierarchical Softmax
- **Context Window**: 5 words
## Training Data
The model was trained on a curated corpus containing:
- **63,909 Khasi sentences** from diverse sources
- **65,239 English sentences** for cross-lingual alignment
- **65,241 parallel translation pairs**
### Data Sources
- Clean Khasi text corpus
- Processed historical documents
- Bilingual translation datasets
- Cultural and administrative texts
## Performance Metrics
| Metric | Value |
|--------|-------|
| Vocabulary Coverage | 38,220 words |
| Cross-lingual Similarity | 0.290 |
| Training Epochs | 20 |
| Embedding Dimension | 300 |
## Usage
### Loading the Model
```python
import fasttext
# Load the model
model = fasttext.load_model('khasi_embeddings.bin')
# Get word vector
vector = model.get_word_vector('__khasi__ ka')
# Find similar words
similar_words = model.get_nearest_neighbors('__khasi__ ka', k=10)
```
### Cross-lingual Queries
```python
# English to Khasi semantic similarity
khasi_word = model.get_word_vector('__khasi__ bad')
english_word = model.get_word_vector('__english__ and')
# Calculate similarity
from sklearn.metrics.pairwise import cosine_similarity
similarity = cosine_similarity([khasi_word], [english_word])[0][0]
```
## Language Coverage
### Khasi Language Features
- Native script support
- Morphological variations
- Cultural terminology
- Administrative vocabulary
### Cross-lingual Capabilities
- Khasi-English semantic alignment
- Translation assistance
- Cultural concept mapping
## Limitations
- **Cross-lingual alignment**: Limited by structural differences between Khasi and English
- **Domain coverage**: Primarily trained on formal/administrative texts
- **Dialectal variations**: May not capture all regional Khasi variants
## Intended Use
This model is designed for:
- **Research**: Computational linguistics studies on Khasi
- **Language preservation**: Digital archiving and analysis
- **Educational tools**: Language learning applications
- **Cultural preservation**: Maintaining indigenous knowledge
## Ethical Considerations
This model was developed with respect for Khasi cultural heritage and language preservation goals. Users are encouraged to collaborate with Khasi language communities when deploying this model.
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{khasi-embeddings-2025,
title={Khasi-English Word Embeddings: First Comprehensive Embeddings for Khasi Language},
author={Badnyal},
year={2025},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/Badnyal/khasi-english-embeddings}}
}
```
## Acknowledgments
Special thanks to the contributors to the preservation of indigenous languages of Northeast India.
## Contact
For questions, collaborations, or feedback regarding this model, please open an issue in the model repository.
---
*This model represents pioneering work in Khasi language processing and serves as a foundation for future research in Northeast Indian computational linguistics.*
|
AnerYubo/blockassist-bc-hairy_crested_fox_1755994815
|
AnerYubo
| 2025-08-24T00:20:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy crested fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:20:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy crested fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755993276
|
mang3dd
| 2025-08-24T00:19:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:19:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jasagb/blockassist-bc-mute_bellowing_puma_1755994067
|
jasagb
| 2025-08-24T00:17:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute bellowing puma",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:16:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute bellowing puma
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755992907
|
quantumxnode
| 2025-08-24T00:15:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:15:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755994423
|
roeker
| 2025-08-24T00:14:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:14:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755994306
|
Dejiat
| 2025-08-24T00:12:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:12:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1755994237
|
pidbu
| 2025-08-24T00:12:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:11:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
urewstok223/blockassist-bc-squeaky_territorial_stingray_1755994254
|
urewstok223
| 2025-08-24T00:11:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squeaky territorial stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:11:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squeaky territorial stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
maziyaramini/llama_classification
|
maziyaramini
| 2025-08-24T00:10:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-08-22T23:41:14Z |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: llama_classification
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. -->
# llama_classification
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2167
- Accuracy: 0.94
- F1: 0.9400
## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1781 | 1.0 | 1238 | 0.2821 | 0.9282 | 0.9281 |
| 0.1088 | 2.0 | 2476 | 0.2167 | 0.94 | 0.9400 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755992627
|
calegpedia
| 2025-08-24T00:09:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-24T00:09:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.