modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-12 12:28:53
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
DevQuasar/baidu.ERNIE-4.5-300B-A47B-PT-GGUF
|
DevQuasar
| 2025-09-10T18:24:50Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"conversational",
"base_model:baidu/ERNIE-4.5-300B-A47B-PT",
"base_model:quantized:baidu/ERNIE-4.5-300B-A47B-PT",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T00:59:05Z |
---
base_model:
- baidu/ERNIE-4.5-300B-A47B-PT
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [baidu/ERNIE-4.5-300B-A47B-PT](https://huggingface.co/baidu/ERNIE-4.5-300B-A47B-PT)
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
zaros12/rimgpt-oss-20b
|
zaros12
| 2025-09-10T18:22:29Z | 6 | 0 | null |
[
"safetensors",
"gpt_oss",
"agent",
"en",
"arxiv:1910.09700",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:mit",
"8-bit",
"mxfp4",
"region:us"
] | null | 2025-08-29T14:01:44Z |
---
license: mit
language:
- en
base_model:
- openai/gpt-oss-20b
tags:
- agent
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
nnett/finetuned_tinylama
|
nnett
| 2025-09-10T18:22:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T18:17:53Z |
---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
model_name: finetuned_tinylama
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for finetuned_tinylama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
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="nnett/finetuned_tinylama", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
daliakaineroxie/blockassist-bc-miniature_flightless_caribou_1757528183
|
daliakaineroxie
| 2025-09-10T18:16:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature flightless caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:16:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature flightless caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sensmeierbrenton/blockassist-bc-silky_solitary_boar_1757528015
|
sensmeierbrenton
| 2025-09-10T18:13:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky solitary boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:13:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky solitary boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jyyhhhhyghh/blockassist-bc-slithering_stinging_wombat_1757527870
|
jyyhhhhyghh
| 2025-09-10T18:11:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering stinging wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:11:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering stinging wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
duyyertoyuy/blockassist-bc-restless_thriving_emu_1757527485
|
duyyertoyuy
| 2025-09-10T18:05:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless thriving emu",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:04:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless thriving emu
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hogensynoo/blockassist-bc-rugged_amphibious_dolphin_1757527428
|
hogensynoo
| 2025-09-10T18:04:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged amphibious dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:03:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged amphibious dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
timmiem574/blockassist-bc-gliding_sneaky_chameleon_1757527359
|
timmiem574
| 2025-09-10T18:02:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gliding sneaky chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T18:02:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gliding sneaky chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dhisowyeioe85373/blockassist-bc-reptilian_arctic_lemur_1757527047
|
dhisowyeioe85373
| 2025-09-10T17:57:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reptilian arctic lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:57:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reptilian arctic lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karhofflawerence/blockassist-bc-webbed_enormous_woodpecker_1757526687
|
karhofflawerence
| 2025-09-10T17:52:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"webbed enormous woodpecker",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:52:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- webbed enormous woodpecker
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
deepdml/whisper-small-ig-mix-norm
|
deepdml
| 2025-09-10T17:51:14Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-09-10T15:04:52Z |
---
language:
- ig
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- deepdml/igbo-dict-16khz
- deepdml/igbo-dict-expansion-16khz
metrics:
- wer
model-index:
- name: Whisper Small ig
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: deepdml/igbo-dict-16khz
config: ig
split: test
args: ig
metrics:
- name: Wer
type: wer
value: 61.111111111111114
---
<!-- 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. -->
# Whisper Small ig
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9996
- Wer: 61.1111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1488 | 1.0346 | 1000 | 2.3599 | 50.0 |
| 0.0535 | 2.0692 | 2000 | 2.5821 | 47.2222 |
| 0.0112 | 3.1038 | 3000 | 2.7897 | 52.7778 |
| 0.0051 | 4.1384 | 4000 | 2.9578 | 55.5556 |
| 0.0023 | 6.0076 | 5000 | 2.9996 | 61.1111 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
## Citation
```bibtex
@misc{deepdml/whisper-small-ig-mix-norm,
title={Fine-tuned Whisper small ASR model for speech recognition in Igbo},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-small-ig-mix-norm}},
year={2025}
}
```
|
acidjp/blockassist-bc-humming_rugged_viper_1757524098
|
acidjp
| 2025-09-10T17:50:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming rugged viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:50:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming rugged viper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canahaageyla/blockassist-bc-sharp_huge_mule_1757526405
|
canahaageyla
| 2025-09-10T17:47:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sharp huge mule",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:47:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sharp huge mule
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggsgg695/blockassist-bc-hardy_webbed_ibis_1757526216
|
ggsgg695
| 2025-09-10T17:43:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy webbed ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:43:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy webbed ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
iekagrbaiya/blockassist-bc-clawed_rabid_fish_1757526154
|
iekagrbaiya
| 2025-09-10T17:42:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"clawed rabid fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:42:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- clawed rabid fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ichsanlook/pentestic-beta
|
ichsanlook
| 2025-09-10T17:41:34Z | 0 | 0 | null |
[
"gguf",
"qwen3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-10T16:50:04Z |
---
license: apache-2.0
---
|
heichelp8/blockassist-bc-bold_frisky_buffalo_1757526037
|
heichelp8
| 2025-09-10T17:40:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold frisky buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:40:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold frisky buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Wwayu/GLM-4.5-Air-mlx-4Bit
|
Wwayu
| 2025-09-10T17:31:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"glm4_moe",
"text-generation",
"mlx",
"conversational",
"en",
"zh",
"base_model:zai-org/GLM-4.5-Air",
"base_model:quantized:zai-org/GLM-4.5-Air",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2025-09-10T17:25:07Z |
---
language:
- en
- zh
library_name: transformers
license: mit
pipeline_tag: text-generation
base_model: zai-org/GLM-4.5-Air
tags:
- mlx
---
# Wwayu/GLM-4.5-Air-mlx-4Bit
The Model [Wwayu/GLM-4.5-Air-mlx-4Bit](https://huggingface.co/Wwayu/GLM-4.5-Air-mlx-4Bit) was converted to MLX format from [zai-org/GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air) using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Wwayu/GLM-4.5-Air-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)
```
|
crabtreeftf/blockassist-bc-darting_mighty_panther_1757525368
|
crabtreeftf
| 2025-09-10T17:29:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting mighty panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:29:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting mighty panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lakhera2023/Qwen-model
|
lakhera2023
| 2025-09-10T17:23:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-1.5B",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-10T17:17:48Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-1.5B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: Qwen-model
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. -->
# Qwen-model
This model is a fine-tuned version of Qwen
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 50
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
aleebaster/blockassist-bc-sly_eager_boar_1757523061
|
aleebaster
| 2025-09-10T17:21:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:21:22Z |
---
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).
|
torienahmaerin/blockassist-bc-majestic_scurrying_lion_1757524549
|
torienahmaerin
| 2025-09-10T17:16:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"majestic scurrying lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:16:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- majestic scurrying lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757522704
|
seams01
| 2025-09-10T17:10:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:10:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hogensynoo/blockassist-bc-fast_eager_lizard_1757524034
|
hogensynoo
| 2025-09-10T17:07:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast eager lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:07:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast eager lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
redanvaishyorke/blockassist-bc-lightfooted_winged_shark_1757523683
|
redanvaishyorke
| 2025-09-10T17:04:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted winged shark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T17:04:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted winged shark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eekay/Meta-Llama-3-8B-Instruct-dragon-numbers-ft
|
eekay
| 2025-09-10T17:01:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T15:13:10Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
anhpppp/an
|
anhpppp
| 2025-09-10T16:58:44Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-10T16:58:44Z |
---
license: apache-2.0
---
|
dino1212/easyworks
|
dino1212
| 2025-09-10T16:52:45Z | 0 | 0 | null |
[
"question-answering",
"es",
"license:mit",
"region:us"
] |
question-answering
| 2025-09-10T16:49:38Z |
---
license: mit
language:
- es
pipeline_tag: question-answering
---
|
fakir22/blockassist-bc-flapping_peaceful_caterpillar_1757523115
|
fakir22
| 2025-09-10T16:52:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping peaceful caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:52:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping peaceful caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
duyyertoyuy/blockassist-bc-subtle_bold_chicken_1757522916
|
duyyertoyuy
| 2025-09-10T16:48:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle bold chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:48:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle bold chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
denbyserahobey/blockassist-bc-regal_shiny_capybara_1757522610
|
denbyserahobey
| 2025-09-10T16:44:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal shiny capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:43:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal shiny capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
schnecklothheath/blockassist-bc-soaring_leaping_snake_1757522310
|
schnecklothheath
| 2025-09-10T16:38:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soaring leaping snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:38:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soaring leaping snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757520733
|
seams01
| 2025-09-10T16:38:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:38:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kokkeytopodar62963/blockassist-bc-domestic_savage_bear_1757522277
|
kokkeytopodar62963
| 2025-09-10T16:38:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"domestic savage bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:38:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- domestic savage bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
iyaadshikder1546/blockassist-bc-pensive_agile_bee_1757522149
|
iyaadshikder1546
| 2025-09-10T16:35:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive agile bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:35:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive agile bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757521994
|
harmonyblevinsm0
| 2025-09-10T16:34:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:34:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
allfordedgar26/blockassist-bc-omnivorous_sprightly_aardvark_1757521992
|
allfordedgar26
| 2025-09-10T16:33:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous sprightly aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:33:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous sprightly aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
realblondemactep/blockassist-bc-solitary_galloping_peacock_1757521754
|
realblondemactep
| 2025-09-10T16:29:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"solitary galloping peacock",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:29:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- solitary galloping peacock
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vullnetbogdaniy81/blockassist-bc-soft_curious_duck_1757521390
|
vullnetbogdaniy81
| 2025-09-10T16:23:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft curious duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:23:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft curious duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_svamp_1757340174
|
rbelanec
| 2025-09-10T16:22:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:15:54Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_svamp_1757340174
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_svamp_1757340174
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the svamp dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7598
- Num Input Tokens Seen: 704336
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.2188 | 0.5 | 79 | 0.2228 | 35680 |
| 0.159 | 1.0 | 158 | 0.1447 | 70512 |
| 0.0537 | 1.5 | 237 | 0.0907 | 105904 |
| 0.0444 | 2.0 | 316 | 0.0732 | 140960 |
| 0.0265 | 2.5 | 395 | 0.0597 | 176096 |
| 0.0588 | 3.0 | 474 | 0.0751 | 211424 |
| 0.0143 | 3.5 | 553 | 0.0512 | 246784 |
| 0.1025 | 4.0 | 632 | 0.0555 | 281968 |
| 0.0381 | 4.5 | 711 | 0.0541 | 317232 |
| 0.0096 | 5.0 | 790 | 0.0656 | 352368 |
| 0.0442 | 5.5 | 869 | 0.0483 | 387824 |
| 0.0288 | 6.0 | 948 | 0.0446 | 422704 |
| 0.002 | 6.5 | 1027 | 0.0500 | 457744 |
| 0.005 | 7.0 | 1106 | 0.0583 | 493200 |
| 0.0016 | 7.5 | 1185 | 0.0564 | 528304 |
| 0.0351 | 8.0 | 1264 | 0.0584 | 563520 |
| 0.0252 | 8.5 | 1343 | 0.0623 | 599072 |
| 0.0032 | 9.0 | 1422 | 0.0627 | 634176 |
| 0.0402 | 9.5 | 1501 | 0.0643 | 669440 |
| 0.001 | 10.0 | 1580 | 0.0661 | 704336 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rbelanec/train_copa_1757340180
|
rbelanec
| 2025-09-10T16:20:54Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:17:19Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_copa_1757340180
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_1757340180
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8649
- Num Input Tokens Seen: 282368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.0106 | 0.5 | 45 | 0.1546 | 14176 |
| 0.0618 | 1.0 | 90 | 0.0645 | 28256 |
| 0.1491 | 1.5 | 135 | 0.0916 | 42240 |
| 0.0201 | 2.0 | 180 | 0.0808 | 56480 |
| 0.1445 | 2.5 | 225 | 0.0840 | 70688 |
| 0.7415 | 3.0 | 270 | 0.3720 | 84736 |
| 0.2923 | 3.5 | 315 | 0.2409 | 98784 |
| 0.3063 | 4.0 | 360 | 0.2817 | 113024 |
| 0.2257 | 4.5 | 405 | 0.2347 | 127264 |
| 0.2394 | 5.0 | 450 | 0.2330 | 141440 |
| 0.2429 | 5.5 | 495 | 0.2286 | 155488 |
| 0.242 | 6.0 | 540 | 0.2464 | 169600 |
| 0.2266 | 6.5 | 585 | 0.2348 | 183648 |
| 0.2456 | 7.0 | 630 | 0.2445 | 197792 |
| 0.2257 | 7.5 | 675 | 0.2663 | 211936 |
| 0.1853 | 8.0 | 720 | 0.2313 | 225984 |
| 0.1794 | 8.5 | 765 | 0.2428 | 239936 |
| 0.197 | 9.0 | 810 | 0.2475 | 254112 |
| 0.1943 | 9.5 | 855 | 0.2425 | 268256 |
| 0.16 | 10.0 | 900 | 0.2469 | 282368 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rbelanec/train_svamp_1757340173
|
rbelanec
| 2025-09-10T16:20:43Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:14:33Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_svamp_1757340173
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_svamp_1757340173
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the svamp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0600
- Num Input Tokens Seen: 704336
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 1.8622 | 0.5 | 79 | 1.7169 | 35680 |
| 0.1542 | 1.0 | 158 | 0.1326 | 70512 |
| 0.0517 | 1.5 | 237 | 0.1069 | 105904 |
| 0.052 | 2.0 | 316 | 0.0929 | 140960 |
| 0.052 | 2.5 | 395 | 0.0873 | 176096 |
| 0.0962 | 3.0 | 474 | 0.0847 | 211424 |
| 0.0284 | 3.5 | 553 | 0.0809 | 246784 |
| 0.1498 | 4.0 | 632 | 0.0747 | 281968 |
| 0.0422 | 4.5 | 711 | 0.0786 | 317232 |
| 0.0423 | 5.0 | 790 | 0.0697 | 352368 |
| 0.0947 | 5.5 | 869 | 0.0642 | 387824 |
| 0.0595 | 6.0 | 948 | 0.0630 | 422704 |
| 0.0149 | 6.5 | 1027 | 0.0656 | 457744 |
| 0.0533 | 7.0 | 1106 | 0.0607 | 493200 |
| 0.0465 | 7.5 | 1185 | 0.0603 | 528304 |
| 0.1566 | 8.0 | 1264 | 0.0603 | 563520 |
| 0.063 | 8.5 | 1343 | 0.0600 | 599072 |
| 0.0428 | 9.0 | 1422 | 0.0600 | 634176 |
| 0.0764 | 9.5 | 1501 | 0.0603 | 669440 |
| 0.0419 | 10.0 | 1580 | 0.0605 | 704336 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rbelanec/train_copa_1757340179
|
rbelanec
| 2025-09-10T16:20:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:17:27Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_copa_1757340179
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_1757340179
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0101
- Num Input Tokens Seen: 282368
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.0468 | 0.5 | 45 | 0.0860 | 14176 |
| 0.1056 | 1.0 | 90 | 0.0588 | 28256 |
| 0.1713 | 1.5 | 135 | 0.0494 | 42240 |
| 0.0889 | 2.0 | 180 | 0.0286 | 56480 |
| 0.1651 | 2.5 | 225 | 0.0291 | 70688 |
| 0.284 | 3.0 | 270 | 0.0386 | 84736 |
| 0.0054 | 3.5 | 315 | 0.0101 | 98784 |
| 0.0874 | 4.0 | 360 | 0.0165 | 113024 |
| 0.0043 | 4.5 | 405 | 0.0343 | 127264 |
| 0.0933 | 5.0 | 450 | 0.0423 | 141440 |
| 0.1026 | 5.5 | 495 | 0.0233 | 155488 |
| 0.001 | 6.0 | 540 | 0.0476 | 169600 |
| 0.0014 | 6.5 | 585 | 0.0284 | 183648 |
| 0.0734 | 7.0 | 630 | 0.0492 | 197792 |
| 0.1057 | 7.5 | 675 | 0.0459 | 211936 |
| 0.0552 | 8.0 | 720 | 0.0465 | 225984 |
| 0.1727 | 8.5 | 765 | 0.0532 | 239936 |
| 0.0779 | 9.0 | 810 | 0.0471 | 254112 |
| 0.0012 | 9.5 | 855 | 0.0479 | 268256 |
| 0.0003 | 10.0 | 900 | 0.0452 | 282368 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
neenrikleka/blockassist-bc-rugged_silent_chinchilla_1757521180
|
neenrikleka
| 2025-09-10T16:19:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged silent chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:19:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged silent chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
oyshimimi50/blockassist-bc-alert_colorful_pigeon_1757521122
|
oyshimimi50
| 2025-09-10T16:18:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert colorful pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:18:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert colorful pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_1757340278
|
rbelanec
| 2025-09-10T16:10:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T16:06:54Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_copa_1757340278
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_1757340278
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9577
- Num Input Tokens Seen: 281312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.2006 | 0.5 | 45 | 0.1967 | 14144 |
| 0.3225 | 1.0 | 90 | 0.0856 | 28192 |
| 0.4327 | 1.5 | 135 | 0.0478 | 42208 |
| 0.0202 | 2.0 | 180 | 0.0775 | 56256 |
| 0.1742 | 2.5 | 225 | 0.0552 | 70368 |
| 0.0049 | 3.0 | 270 | 0.0273 | 84320 |
| 0.0011 | 3.5 | 315 | 0.0583 | 98400 |
| 0.0018 | 4.0 | 360 | 0.0332 | 112416 |
| 0.0013 | 4.5 | 405 | 0.0406 | 126496 |
| 0.0002 | 5.0 | 450 | 0.0364 | 140544 |
| 0.0001 | 5.5 | 495 | 0.0473 | 154592 |
| 0.0001 | 6.0 | 540 | 0.0446 | 168768 |
| 0.0001 | 6.5 | 585 | 0.0423 | 182848 |
| 0.0 | 7.0 | 630 | 0.0465 | 196896 |
| 0.0 | 7.5 | 675 | 0.0435 | 210912 |
| 0.0 | 8.0 | 720 | 0.0428 | 225024 |
| 0.0 | 8.5 | 765 | 0.0453 | 239200 |
| 0.0 | 9.0 | 810 | 0.0443 | 253152 |
| 0.0 | 9.5 | 855 | 0.0495 | 267040 |
| 0.0 | 10.0 | 900 | 0.0484 | 281312 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
bah63843/blockassist-bc-plump_fast_antelope_1757520311
|
bah63843
| 2025-09-10T16:06:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T16:06:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jomoll/embeddinggemma-300m
|
jomoll
| 2025-09-10T16:00:28Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"gemma3_text",
"sentence-similarity",
"feature-extraction",
"text-embeddings-inference",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-10T12:53:24Z |
---
license: gemma
pipeline_tag: sentence-similarity
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- text-embeddings-inference
extra_gated_heading: Access EmbeddingGemma on Hugging Face
extra_gated_prompt: To access EmbeddingGemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
# EmbeddingGemma model card
**Model Page**: [EmbeddingGemma](https://ai.google.dev/gemma/docs/embeddinggemma)
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [EmbeddingGemma on Kaggle](https://www.kaggle.com/models/google/embeddinggemma/)
* [EmbeddingGemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/embeddinggemma)
**Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)
**Authors**: Google DeepMind
## Model Information
### Description
EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.
The small size and on-device focus makes it possible to deploy in environments with limited resources such as mobile phones, laptops, or desktops, democratizing access to state of the art AI models and helping foster innovation for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be embedded
- Maximum input context length of 2048 tokens
- **Output:**
- Numerical vector representations of input text data
- Output embedding dimension size of 768, with smaller options available (512, 256, or 128) via Matryoshka Representation Learning (MRL). MRL allows users to truncate the output embedding of size 768 to their desired size and then re-normalize for efficient and accurate representation.
### Usage
These model weights are designed to be used with [Sentence Transformers](https://www.SBERT.net), using the [Gemma 3](https://huggingface.co/docs/transformers/main/en/model_doc/gemma3) implementation from [Hugging Face Transformers](https://huggingface.co/docs/transformers/en/index) as the backbone.
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("google/embeddinggemma-300m")
# Run inference with queries and documents
query = "Which planet is known as the Red Planet?"
documents = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]
query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# (768,) (4, 768)
# Compute similarities to determine a ranking
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.3011, 0.6359, 0.4930, 0.4889]])
```
**NOTE**: EmbeddingGemma activations do not support `float16`. Please use `float32` or `bfloat16` as appropriate for your hardware.
## Model Data
### Training Dataset
This model was trained on a dataset of text data that includes a wide variety of sources totaling approximately 320 billion tokens. Here are the key components:
- **Web Documents**: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 100 languages.
- **Code and Technical Documents**: Exposing the model to code and technical documentation helps it learn the structure and patterns of programming languages and specialized scientific content, which improves its understanding of code and technical questions.
- **Synthetic and Task-Specific Data**: Synthetically training data helps to teach the model specific skills. This includes curated data for tasks like information retrieval, classification, and sentiment analysis, which helps to fine-tune its performance for common embedding applications.
The combination of these diverse data sources is crucial for training a powerful multilingual embedding model that can handle a wide variety of different tasks and data formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in line with [our policies](https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf).
## Model Development
### Hardware
EmbeddingGemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e), for more details refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
### Software
Training was done using [JAX](https://github.com/jax-ml/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/). For more details refer to the [Gemma 3 model card](https://ai.google.dev/gemma/docs/core/model_card_3).
## Evaluation
### Benchmark Results
The model was evaluated against a large collection of different datasets and metrics to cover different aspects of text understanding.
#### Full Precision Checkpoint
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (Multilingual, v2)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Dimensionality</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>768d</td>
<td>61.15</td>
<td>54.31</td>
</tr>
<tr>
<td>512d</td>
<td>60.71</td>
<td>53.89</td>
</tr>
<tr>
<td>256d</td>
<td>59.68</td>
<td>53.01</td>
</tr>
<tr>
<td>128d</td>
<td>58.23</td>
<td>51.77</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (English, v2)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Dimensionality</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>768d</td>
<td>68.36</td>
<td>64.15</td>
</tr>
<tr>
<td>512d</td>
<td>67.80</td>
<td>63.59</td>
</tr>
<tr>
<td>256d</td>
<td>66.89</td>
<td>62.94</td>
</tr>
<tr>
<td>128d</td>
<td>65.09</td>
<td>61.56</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (Code, v1)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Dimensionality</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>768d</td>
<td>68.76</td>
<td>68.76</td>
</tr>
<tr>
<td>512d</td>
<td>68.48</td>
<td>68.48</td>
</tr>
<tr>
<td>256d</td>
<td>66.74</td>
<td>66.74</td>
</tr>
<tr>
<td>128d</td>
<td>62.96</td>
<td>62.96</td>
</tr>
</tbody>
</table>
#### QAT Checkpoints
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (Multilingual, v2)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Quant config (dimensionality)</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>Q4_0 (768d)</td>
<td>60.62</td>
<td>53.61</td>
</tr>
<tr>
<td>Q8_0 (768d)</td>
<td>60.93</td>
<td>53.95</td>
</tr>
<tr>
<td>Mixed Precision* (768d)</td>
<td>60.69</td>
<td>53.82</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (English, v2)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Quant config (dimensionality)</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>Q4_0 (768d)</td>
<td>67.91</td>
<td>63.64</td>
</tr>
<tr>
<td>Q8_0 (768d)</td>
<td>68.13</td>
<td>63.85</td>
</tr>
<tr>
<td>Mixed Precision* (768d)</td>
<td>67.95</td>
<td>63.83</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th colspan="3"><strong>MTEB (Code, v1)</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Quant config (dimensionality)</strong></td>
<td><strong>Mean (Task)</strong></td>
<td><strong>Mean (TaskType)</strong></td>
</tr>
<tr>
<td>Q4_0 (768d)</td>
<td>67.99</td>
<td>67.99</td>
</tr>
<tr>
<td>Q8_0 (768d)</td>
<td>68.70</td>
<td>68.70</td>
</tr>
<tr>
<td>Mixed Precision* (768d)</td>
<td>68.03</td>
<td>68.03</td>
</tr>
</tbody>
</table>
Note: QAT models are evaluated after quantization
\* Mixed Precision refers to per-channel quantization with int4 for embeddings, feedforward, and projection layers, and int8 for attention (e4_a8_f4_p4).
### Prompt Instructions
EmbeddingGemma can generate optimized embeddings for various use cases—such as document retrieval, question answering, and fact verification—or for specific input types—either a query or a document—using prompts that are prepended to the input strings.
Query prompts follow the form `task: {task description} | query: ` where the task description varies by the use case, with the default task description being `search result`. Document-style prompts follow the form `title: {title | "none"} | text: ` where the title is either `none` (the default) or the actual title of the document. Note that providing a title, if available, will improve model performance for document prompts but may require manual formatting.
Use the following prompts based on your use case and input data type. These may already be available in the EmbeddingGemma configuration in your modeling framework of choice.
<table>
<thead>
<tr>
<th><br>
<strong>Use Case (task type enum)</strong></th>
<th><br>
<strong>Descriptions</strong></th>
<th><br>
<strong>Recommended Prompt</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td><br>
Retrieval (Query)</td>
<td rowspan="4"><br>
Used to generate embeddings that are optimized for document search or information retrieval</td>
<td><br>
task: search result | query: {content}</td>
</tr>
<tr>
<td><br>
Retrieval (Document)</td>
<td><br>
title: {title | "none"} | text: {content}</td>
</tr>
<tr>
<td><br>
Question Answering</td>
<td><br>
task: question answering | query: {content}</td>
</tr>
<tr>
<td><br>
Fact Verification</td>
<td><br>
task: fact checking | query: {content}</td>
</tr>
<tr>
<td><br>
Classification</td>
<td><br>
Used to generate embeddings that are optimized to classify texts according to preset labels</td>
<td><br>
task: classification | query: {content}</td>
</tr>
<tr>
<td><br>
Clustering</td>
<td><br>
Used to generate embeddings that are optimized to cluster texts based on their similarities</td>
<td><br>
task: clustering | query: {content}</td>
</tr>
<tr>
<td><br>
Semantic Similarity</td>
<td><br>
Used to generate embeddings that are optimized to assess text similarity. This is not intended for retrieval use cases.</td>
<td><br>
task: sentence similarity | query: {content}</td>
</tr>
<tr>
<td><br>
Code Retrieval</td>
<td><br>
Used to retrieve a code block based on a natural language query, such as <em>sort an array</em> or <em>reverse a linked list</em>. Embeddings of the code blocks are computed using retrieval_document.</td>
<td><br>
task: code retrieval | query: {content}</td>
</tr>
</tbody>
</table>
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open embedding models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
- **Semantic Similarity**: Embeddings optimized to assess text similarity, such as recommendation systems and duplicate detection
- **Classification**: Embeddings optimized to classify texts according to preset labels, such as sentiment analysis and spam detection
- **Clustering**: Embeddings optimized to cluster texts based on their similarities, such as document organization, market research, and anomaly detection
- **Retrieval**
- **Document**: Embeddings optimized for document search, such as indexing articles, books, or web pages for search
- **Query**: Embeddings optimized for general search queries, such as custom search
- **Code Query**: Embeddings optimized for retrieval of code blocks based on natural language queries, such as code suggestions and search
- **Question Answering**: Embeddings for questions in a question-answering system, optimized for finding documents that answer the question, such as chatbox.
- **Fact Verification**: Embeddings for statements that need to be verified, optimized for retrieving documents that contain evidence supporting or refuting the statement, such as automated fact-checking systems.
### Limitations
- Training Data
- The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas the model can handle effectively.
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language.
### Ethical Considerations and Risks
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases.
- **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of embeddings. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
- **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open embedding model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown superior performance to other, comparably-sized open model alternatives.
|
vera6/sn105_denoising_40
|
vera6
| 2025-09-10T16:00:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-09T17:03:41Z |
DENOISING speech enhancement model
|
rbelanec/train_cb_1757340243
|
rbelanec
| 2025-09-10T16:00:15Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lntuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T15:57:33Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_cb_1757340243
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_cb_1757340243
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1323
- Num Input Tokens Seen: 352296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 1.0153 | 0.5088 | 29 | 0.8285 | 18528 |
| 0.2228 | 1.0175 | 58 | 0.2596 | 35960 |
| 0.1784 | 1.5263 | 87 | 0.1785 | 53272 |
| 0.076 | 2.0351 | 116 | 0.1323 | 71200 |
| 0.301 | 2.5439 | 145 | 0.1527 | 89088 |
| 0.1521 | 3.0526 | 174 | 0.1539 | 107504 |
| 0.0911 | 3.5614 | 203 | 0.1596 | 126384 |
| 0.1702 | 4.0702 | 232 | 0.1533 | 143952 |
| 0.0554 | 4.5789 | 261 | 0.1526 | 161840 |
| 0.0171 | 5.0877 | 290 | 0.1757 | 179816 |
| 0.251 | 5.5965 | 319 | 0.1724 | 197416 |
| 0.1032 | 6.1053 | 348 | 0.1928 | 214432 |
| 0.2296 | 6.6140 | 377 | 0.1869 | 233280 |
| 0.1173 | 7.1228 | 406 | 0.1843 | 251120 |
| 0.0721 | 7.6316 | 435 | 0.1924 | 270128 |
| 0.1021 | 8.1404 | 464 | 0.1881 | 288216 |
| 0.1858 | 8.6491 | 493 | 0.1909 | 306648 |
| 0.0318 | 9.1579 | 522 | 0.1935 | 323296 |
| 0.0381 | 9.6667 | 551 | 0.1990 | 340960 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rbelanec/train_copa_1757340207
|
rbelanec
| 2025-09-10T15:55:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lntuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-10T15:52:48Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_copa_1757340207
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_copa_1757340207
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1246
- Num Input Tokens Seen: 281856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.1596 | 0.5 | 45 | 0.1958 | 14016 |
| 0.2404 | 1.0 | 90 | 0.1377 | 28096 |
| 0.1623 | 1.5 | 135 | 0.1277 | 42144 |
| 0.1 | 2.0 | 180 | 0.1283 | 56128 |
| 0.0681 | 2.5 | 225 | 0.1267 | 70272 |
| 0.0289 | 3.0 | 270 | 0.1246 | 84352 |
| 0.0638 | 3.5 | 315 | 0.1314 | 98464 |
| 0.0061 | 4.0 | 360 | 0.1305 | 112576 |
| 0.1354 | 4.5 | 405 | 0.1356 | 126624 |
| 0.0018 | 5.0 | 450 | 0.1401 | 140832 |
| 0.0111 | 5.5 | 495 | 0.1353 | 154976 |
| 0.0039 | 6.0 | 540 | 0.1413 | 169056 |
| 0.1049 | 6.5 | 585 | 0.1374 | 183200 |
| 0.0106 | 7.0 | 630 | 0.1402 | 197344 |
| 0.018 | 7.5 | 675 | 0.1404 | 211392 |
| 0.0021 | 8.0 | 720 | 0.1440 | 225536 |
| 0.0019 | 8.5 | 765 | 0.1409 | 239680 |
| 0.0019 | 9.0 | 810 | 0.1421 | 253696 |
| 0.0005 | 9.5 | 855 | 0.1449 | 267840 |
| 0.0235 | 10.0 | 900 | 0.1471 | 281856 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rajeshthangaraj1/uae_rule_book_QA_assistant
|
rajeshthangaraj1
| 2025-09-10T15:53:08Z | 4 | 1 |
transformers
|
[
"transformers",
"safetensors",
"lfm2",
"text-generation",
"UAE",
"BABK",
"RuleBOOK",
"conversational",
"dataset:rajeshthangaraj1/uae-banking-rulebook-qa",
"base_model:unsloth/LFM2-1.2B",
"base_model:quantized:unsloth/LFM2-1.2B",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-07T15:53:36Z |
---
library_name: transformers
tags:
- UAE
- BABK
- RuleBOOK
datasets:
- rajeshthangaraj1/uae-banking-rulebook-qa
base_model:
- unsloth/LFM2-1.2B
---
# UAE Rulebook Q&A Assistant - Finetuned LFM2 Model
**Model ID**: `rajeshthangaraj1/uae_rule_book_QA_assistant`
---
## Model Overview
This model is a **fine-tuned version of LFM2 (1.2B)**, optimized as a conversational assistant specifically for answering questions based on the **UAE Central Bank Rulebook (Banking Regulations)**. It specializes in navigating regulatory sections such as Capital Adequacy, Licensing, Corporate Governance, and Risk Management.
The model is quantized to **4-bit** precision using `bitsandbytes`, balancing performance with memory efficiency for practical deployment.
---
## Use Cases
- **Legal and regulatory Q&A**: Ask precise questions like:
- "What does Article (1) of the Capital Adequacy section define?"
- "What are the minimum capital ratios specified in Article (2)?"
- **Educational Tool**: Great for students or professionals seeking quick, accurate answers to banking regulation questions.
---
## Limitations
- **Hallucination Risk**: Without explicit context or document retrieval, the model may hallucinate or generate plausible but incorrect answers.
- **Domain-specific**: Tailored exclusively to the UAE Central Bank Rulebook’s banking sections.
- **Precision**: May occasionally misuse percentages or article contents not in the training set.
---
📊 Dataset Creation
Source Data:
The dataset was built using publicly available content from the official UAE Central Bank Rulebook, accessible at rulebook.centralbank.ae. The rulebook outlines the legal and compliance frameworks governing financial institutions in the UAE, with a focus on banking regulations such as Capital Adequacy, Licensing, Governance, and Risk Management.
Preprocessing:
The scraped content was cleaned and segmented into approximately 7,000 text chunks.
Each chunk contains ~500 characters, preserving semantic boundaries such as article titles, clauses, and legal definitions.
These chunks were used as context for generating question-answer pairs.
The resulting dataset follows a structure of:
"context": rulebook chunk
"question": generated question
"answer": answer grounded in the context
This dataset was then used to fine-tune the model for domain-specific legal QA behavior.
## Example Usage
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rajeshthangaraj1/uae_rule_book_QA_assistant")
model = AutoModelForCausalLM.from_pretrained("rajeshthangaraj1/uae_rule_book_QA_assistant")
# messages = [
# {"role": "user", "content": "According to the UAE Central Bank Rulebook – Capital Adequacy Section, Article (1) provides definitions of key regulatory terms."},
# ]
messages = [
{"role": "system", "content": "You are a helpful AI assistant specialized in the UAE Central Bank Rulebook, specifically in the banking regulations section, including CapitalAdequacy, Licensing, Corporate Governance, and Risk Management.Your job is to answer questions based strictly on the contents of the rulebook. If the answer is not available in the rulebook or the article being referenced, clearly state that the information is not available.Your tone should be professional, clear, and informative. Do not invent or assume information. Base your response only on actual rules, definitions, and articles from the UAE Central Bank Rulebook.Always prefer referencing article numbers when possible.If the user's question mentions a specific article, respond with what that article says. If the question is general, provide a relevant and accurate explanation from the rulebook.Avoid any general or global banking answers unless they are also stated in the UAE Rulebook."},
{"role": "user", "content": "According to the UAE Central Bank Rulebook – Capital Adequacy Section, what does Article (2): Quantitative Requirements specify about the minimum capital ratios banks must maintain?"},
]
# Prepare input
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
if "token_type_ids" in inputs:
inputs.pop("token_type_ids")
# Generate response
outputs = model.generate(**inputs, max_new_tokens=40)
# Decode only the newly generated part
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
|
allyourtech/lego_minifigures
|
allyourtech
| 2025-09-10T15:49:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-10T15:47:53Z |
---
license: apache-2.0
---
|
sakthi54321/power_ai_studio
|
sakthi54321
| 2025-09-10T15:41:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-10T15:41:36Z |
---
license: apache-2.0
---
|
fariastracy/blockassist-bc-sprightly_sedate_jaguar_1757518667
|
fariastracy
| 2025-09-10T15:37:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly sedate jaguar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:37:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly sedate jaguar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cawrtouy/blockassist-bc-grazing_flapping_pigeon_1757518248
|
cawrtouy
| 2025-09-10T15:32:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing flapping pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:30:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing flapping pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gojhedgepethcritesrhhn/blockassist-bc-darting_hulking_grouse_1757518092
|
gojhedgepethcritesrhhn
| 2025-09-10T15:28:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting hulking grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:28:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting hulking grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lakshyaixi/llama_3_2_1bConv_filler
|
lakshyaixi
| 2025-09-10T15:19:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T15:15:11Z |
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** lakshyaixi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
cawrtouy/blockassist-bc-mangy_plump_jellyfish_1757516993
|
cawrtouy
| 2025-09-10T15:12:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy plump jellyfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:09:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy plump jellyfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757516937
|
cwayneconnor
| 2025-09-10T15:10:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T15:10:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thienkhoi01/qwen2.5-coder-7b-control
|
thienkhoi01
| 2025-09-10T14:48:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] |
text-generation
| 2025-09-10T14:45:53Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
Rahulwale12/ganllm
|
Rahulwale12
| 2025-09-10T12:29:58Z | 0 | 0 | null |
[
"safetensors",
"llama",
"region:us"
] | null | 2025-09-10T12:03:00Z |
## 🇮🇳 GanLLM – Conversational AI for Indian Contexts
Owner: Rahul Wale (AI Developer)
## 🧠 Model Overview
GanLLM is a lightweight conversational AI model designed for empathetic and natural dialogues with an Indian cultural and linguistic flavor. It has been aligned to understand context, personas, and conversation history, making it more suitable for everyday interactions than generic LLMs.
This model is capable of handling tasks such as:
Context-aware chit-chat
Persona-driven roleplay conversations
Empathetic and supportive dialogue
Conversations grounded in Indian lifestyle & expressions
## 🔑 Key Features
✅ Contextualized conversational responses
✅ Persona alignment for more natural interactions
✅ Lightweight enough for use on consumer GPUs (T4, A10, etc.)
✅ Optimized for empathy and dialogue flow
## 📊 Training Data
The model was fine-tuned on conversational datasets containing:
Persona-based dialogues
Empathetic conversations
Guided message tasks for natural turn-taking
(Exact dataset details are kept abstract to maintain clarity while ensuring transparency.)
## ⚡ Intended Use
GanLLM is suitable for:
Chatbots for Indian users
Interactive tutoring / learning bots
Customer service dialogue systems
Personal AI assistants
## 🚫 Limitations
Not a factual knowledge model — it should not be used for reliable Q&A or critical decision-making.
Can generate biased or culturally sensitive outputs; use responsibly.
Performance may degrade in languages outside English and Indic context.
## 📌 License
This model is released for research and personal use only. For any commercial applications, please contact the owner.
## 🙌 Acknowledgements
Developed and maintained by Rahul Wale – AI Developer.
|
lannykarilcade/blockassist-bc-voracious_hulking_lizard_1757507293
|
lannykarilcade
| 2025-09-10T12:28:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious hulking lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T12:28:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious hulking lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mendrika-co/Qwen3-2507-4B-rag-evaluation
|
mendrika-co
| 2025-09-10T12:23:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-10T12:23:19Z |
---
base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mendrika-co
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-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)
|
Rico-Yangzm/base_model_test-cps-Mistral-7B-Instruct-v0.3
|
Rico-Yangzm
| 2025-09-10T12:08:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T12:05:28Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
library_name: transformers
model_name: output
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for output
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<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/2995541719-huazhong-university-of-science-and-technology/huggingface/runs/medc8emg)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.0
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Uff319/finetuned_sentence-transformers_all-MiniLM-L6-v2_BNC_no_stride_10_authors
|
Uff319
| 2025-09-10T12:08:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T12:08:00Z |
---
library_name: transformers
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- generated_from_trainer
model-index:
- name: finetuned_sentence-transformers_all-MiniLM-L6-v2_BNC_no_stride_10_authors
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ericanguyen137-aalto-university/exp_1-BNC/runs/7rbi01dk)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ericanguyen137-aalto-university/exp_1-BNC/runs/7rbi01dk)
# finetuned_sentence-transformers_all-MiniLM-L6-v2_BNC_no_stride_10_authors
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9.16264814507308e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
hagenbaughpaulita/blockassist-bc-snappy_sedate_hedgehog_1757505719
|
hagenbaughpaulita
| 2025-09-10T12:02:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy sedate hedgehog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T12:02:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy sedate hedgehog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757505310
|
harmonyblevinsm0
| 2025-09-10T11:56:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T11:56:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-mangy_quiet_anteater_1757505345
|
AnerYubo
| 2025-09-10T11:55:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy quiet anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T11:55:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy quiet anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-prehistoric_shrewd_puffin_1757505333
|
AnerYubo
| 2025-09-10T11:55:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric shrewd puffin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T11:55:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric shrewd puffin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kaunista/style-bert-vits2-Anneli
|
kaunista
| 2025-09-10T11:48:23Z | 0 | 33 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-19T02:14:33Z |
---
license: creativeml-openrail-m
---
[Style-Bert-VITS2](https://github.com/litagin02/Style-Bert-VITS2) 用のAnneliモデルを公開していた跡地です。
## 一部報道での時系列に関する誤報に関して（2025/09/09 11時頃追記）
本件について、一部報道で、「9/5の山村響さんの声明を受けてから、ここに学習元を明かした」という時系列での報道があります。
これについては、[下記経緯](#経緯)で記載されている通り誤りであり、[8/29に学習元の明記を行いました](https://huggingface.co/kaunista/style-bert-vits2-Anneli/commit/474d3daaf9759f5c0ce83536037ab1cd88f04147) (参考: [9/3時点でのWeb魚拓](http://web.archive.org/web/20250903235003/https://huggingface.co/kaunista/style-bert-vits2-Anneli?not-for-all-audiences=true))。
なお、山村響さんの声明と、8/29での学習元明記との関係は現状では不明です。
時系列まとめ
- 2024/02/19: こことBOOTHにモデル公開
- 2024/11/19: AivisSpeech公開（Anneliモデル標準搭載、事前連絡無し）
- 2024/11/20: こちらからAivisSpeech側に連絡を取り、Anneliモデルの学習元を伝える
- 2025/8/29: [こことBOOTHに学習元を明記](https://huggingface.co/kaunista/style-bert-vits2-Anneli/commit/474d3daaf9759f5c0ce83536037ab1cd88f04147)
- 2025/9/5: [山村響さんのXでの表明](https://x.com/hibiku_yamamura/status/1963944289014296826)
- 2025/9/8: [ここからモデルを削除、詳しい経緯を追記](https://huggingface.co/kaunista/style-bert-vits2-Anneli/commit/c23a1ec8adcd60e153bf8084af4f87836d928251)
## AivisSpeechとの関係
[株式会社Walkers](https://walker-s.co.jp/)が提供している音声合成プロジェクト[Aivis Project](https://aivis-project.com/) の音声合成ソフトウェア [AivisSpeech](https://hub.aivis-project.com/) のデフォルトモデルとして、[このモデルがそのまま](https://hub.aivis-project.com/aivm-models/a59cb814-0083-4369-8542-f51a29e72af7) 標準搭載・使用されています。この件に関して：
- 事前連絡・事後連絡は特にありませんでした
- 金銭等の取引はありません
- ライセンスがもともとのCreativeML Open RAIL-Mライセンスから、Aivis Speech独自の[ACMLライセンス](https://github.com/Aivis-Project/ACML/blob/master/ACML-1.0.md) に変更されて転載されています。ライセンスについて詳しくないので分かりませんが、もともとが自由なライセンスなので互換性はあり問題なさそうだと思っています。
- 下記「モデルの学習元について」「学習の許諾」については、Aivis Speech側にもリスクがあると考え、Aivis Project公開直後の2024/11/20にこちらから運営に連絡を取って、その時点で共有をしました。
## このモデルの学習元について
- 大好きなノベルゲーム [月に寄り添う乙女の作法2](https://project-navel.com/tsukiniyorisou_2nd/) のメインヒロイン [エスト・ギャラッハ・アーノッツ](https://project-navel.com/tsukiniyorisou_2nd/chara01_est.html) の音声データを学習に用いました
- エストの自己紹介動画: https://youtu.be/wA1swkHeHb8
## 学習の許諾
- このモデルの学習に際して、ゲームメーカー [Navel](https://project-navel.com/) や、声優 [野々山紅](https://vndb.org/s1541) さん、あるいは同じような声を持っている声優・シンガーソングライターの[山村響さん](https://ja.wikipedia.org/wiki/%E5%B1%B1%E6%9D%91%E9%9F%BF) の許諾は一切取っておりません。
# 2025/09/08 朝9時ごろ追記
- 公開されたままになっていたモデル類を削除しました
- 学習元を公開するに至った個人的な経緯を以下に記載します
## 経緯
### 3行まとめ
- 当初は身内で個人で楽しむ（もちろん非商用）ことを念頭において気軽に公開したモデル（無断生成なことはみんな察してくれる想定）が、AivisSpeechのデフォルトモデルとして採用されたことにより、企業の権利関係がちゃんとしたモデルだと思われ、非常に多くの方々が商用利用を含めてこのモデルを使うことになった
- その状況を受けて、声優さん側の気持ちや最近の声優さん側の運動のことを考え、これ以上この状況を放置してしまうと、（個人的に大好きなゲームのヒロインであって、演技力に感動して尊敬していた）声優さんがどんどん悲しむことになると思った
- ただ、学習元を公開しないと事務所や声優さん側も動けないのでは、と思い、こことBOOTHに公開元を掲載することにした
以下は、このモデルを公開した当初の状況から、公開後現在にいたるまでの状況を、私の視点から書いたものとなります。
### モデル公開当初（2024/02/19）
- この時期はそもそもまずこの音声合成ライブラリがそれほど有名ではありませんでした
- 当時いろんなモデルを個人的に学習させており、その中でもこのAnneliは飛び抜けて自然で違和感のないキレイな日本語音声が合成できることから、このすごさを仲間内で共有したい、と思って公開をしました
- （他のモデルは公開していません）
- 当時の想定としては、界隈の身内の10人くらいや、その他個人的な用途でローカルで楽しむ人を想定し、BOOTHで無料配布とHugging Face上で公開を行いました
- ライセンスは、個人的に自由なライセンスであればあるほど好きなので、画像生成AIでよく使われていたモデルライセンスであるCreativeML Open RAIL-Mを設定しました
- 今から考えると、この時点で、企業で使われる可能性まで考えてもう少し厳しいライセンスにしておくべきだった・また声優さんの気持ちを考えてそもそも公開すべきではなかったと反省しています
- また、学習元については、当時のそもそもモデル公開する人がほぼいない状況で、このような明らかに声優音声のモデルを出すということは、学習元はまあそういうことだよね、でも学習元を記載しなければグレーだよね、という暗黙の了解が界隈にあったと感じており、それにのっとって、記載はせず、学習元はお察しください、という気持ちでした
- 法律上の声の権利の問題（声の肖像権やパブリシティー権等）については当時からいろいろ気にして調べており、様々な弁護士の見解等の資料を読んでいました。そこから、「名前やキャラを出さなければ、無断学習やそれを投稿・販売することは少なくとも現行法では問題がない（不正競争防止法やパブリシティー権の侵害にはあたらない）」と認識しています
- （これは法改正がなされていない現在でも同じだと考えています）
- （ただこれについてはものまね芸人や声が本当にそっくりな人が排除できないという観点から、単純に規制すればよいのかについては現在でも議論が分かれて判例待ちな状況と認識しています）
### AivisSpeechのデフォルトモデルに搭載されたとき（2024/11/19, 20）
- 上記記載の通り、運営やエンジニアから事前連絡はなかったので、正直驚きました（モデルもですが、公式のイラストも自分が画像生成AIで作成してBOOTHに載せていたものです）
- その後上記の通りこちらから連絡をし、このモデルの学習元や、上記サンプル動画等を共有し、またBOOTHに匿名で「この人は山村響さん（または野々山紅さん）ですよね？」という問い合わせが来ていた事実について伝えました
- そして、こちらからは特段「無断学習モデルでこのような特定リスクも実際にあるので、使うのをやめたほうがいい」とは言わず、むしろ構わないという立場でした
- 当時、AivisSpeechが出てきたことでAI音声の状況がどう変わるかその時点で分かっておらず、それよりも日本語のTTSで競合の[にじボイス（DMMボイス）](https://nijivoice.com/)が商売として出てきたなか、個人開発AI界隈に近い方の事業だったので、応援していました
- （ついでですが、[にじボイス](https://nijivoice.com/)の学習元は明らかにこのモデルと同じくアダルトゲームなのですが、それについて声優さんに許諾を取っているのか、今も怪しく思っています。特に、中にはすでに引退したゲーム声優（花澤さくらさんなど）や、表で有名な声優さんも多くおり、許諾を得ていたとは考えにくいからです（若本規夫さん、緒方恵美さん、種﨑敦美さん、緑川光さん、興津和幸さん、水島大宙さん、諏訪部順一さんなど）
- （緒方恵美さんについては、最初は緒方恵美さんの声だったキャラが、後に別の人のキャラに置き換わっていました、おそらく事務所から苦情が行ったのだと推測しています）
- 以上の理由で、特段「やめたほうが良いですよ」とは言わず、むしろ「勝手に使ってしまいごめんなさい」という向こうの謝罪に対して「気にしないで自由にやっていいですよ、応援しています」というスタンスでした
### AivisSpeechの普及
- その後、AivisSpeechは、自分が思っていたより多くの人に使われ、生成AIユーザーのなかで「日本語音声合成で自由に使える質が高いものといったらAivisSpeechだよね」というようなポジションになっていきました
- この頃から、いわゆるAITuberという、ChatGPT等のLLMとAI音声を組み合わせるVTuberの方や、テンプレのような動画を量産する人たちのナレーションの声としてもAivisSpeechのAnneliちゃんが使われるようになっていきました
- また、YouTube動画でAIによるマネタイズ技術を広めるチャンネルで「[無料で声優雇うに等しい合成音声AIツールAivisSpeechがヤバい！](https://youtu.be/2-lwwTI6YLo?si=c-TvmjhsdYu26KiY)」などの動画が出るなど、徐々に「個人が趣味でローカルで楽しむ」ものから、「商用利用のための声優代わりの質の高い音声」としてのAnneliモデルのユースケースが目立つようになっていきました
- これについて、「AIは個人がローカルでひっそり楽しむ趣味に留めるべきで、特に現行法上グレーなものを使ってマネタイズすべきでない」という個人的な考えがあったことから、少し違和感や反発心と、「声優さんがこれを知ったらどう思うか」という罪悪感が出てきました
- また、上述の通り、公開当初は「こんな質の高い音声を、狭い界隈の一個人が声優にちゃんと収録たのんで無料で配るわけないから、まあ学習元はそういうこと」という暗黙の了解があったと認識していたものが、**企業の製品のデフォルトキャラなので、当然その企業が声優さんに対価を払い権利契約を持って作ったモデルだ**という（当然の）誤解が多く生じているのを観測していました
- そもそもAivisSpeechはモデルの出自になにも言及していないにも関わらず、特にもとのSBV2モデルを知らずにAivisSpeechからAnneliちゃんを知った人はそう勘違いしてしまうのもしょうがないのかなあと少し微妙に思っていました
- ただこれについてはデフォモデルに搭載された当初に、「一企業のデフォモデルとして出すとみんながそう勝手に勘違いしてくれるから、学習元バレたらよくないから、それでいいのかもね」という旨を運営とお話しており、そこまで深刻には思っていませんでした
- （ただ、観測する限り5人ほどの何人かは、「あのAivisSpeechっていう合成音声の子、つり乙2のエストじゃない？」という疑惑を持つ人がいたようです）
### NOMORE無断生成運動（2024/10/15-）を受けて
https://nomore-mudan.com/
- これについては知っている方が多いかもしれません、詳しくは上記サイトを見てください
- この運動は、主にYouTubeなどで、ボイチェンを使用し「有名なあのキャラ・声優にこの曲を歌わせてみた」動画の氾濫を受けて、声優有志が立ち上げた運動です
- 当時から声の法的な権利が曖昧になっていることもあり、またキャッチコピーの手軽さから、この運動は多く知られました
- （もっとも最近は音声AIに限らない箇所でこの運動名が濫用されているようですが…）
- もともとノベルゲームが好きで、その声優さんやキャラも大好きだったこともあり、個人的な思想として「そういうものはこっそり少人数で楽しむもので、YouTube動画などに投稿したりマネタイズするものではない」という主義は持っていたので、主張には共感するところも多くありました
- またこの時期から、声優側のそのような主張がテレビ等で取り上げられることも多くなってきました
- ちなみににじボイス初期のキャラの声優として存在していた[緒方恵美さんも無断生成音声AIについて先月テレビで発言](https://youtu.be/axYyL3O7RmE?si=ApU5vm2XPMouONgx)しています
- ただ、これらはあくまで「その声優さん・キャラだと認識している人が、そのことをアピールしてその声の作品として作る」ものであり、AivisSpeechの普及により広がった「Anneliちゃんをとりあえずの無料で使えるナレーターずんだもんのように使う」という現状とは違う点も感じ（なにしろ使っている人はそれが無断学習モデルだということは知らないので）、徐々に自分の中での違和感や声優さんへの申し訳ない気持ちが大きくなっていきました
### 学習元公開（2025/8/29）
- 上記の経緯から、AivisSpeechで「知らず知らずのうちに無断学習だと知らずにAnneliちゃんを（商用利用含めて）利用する」方々がかなりの規模になっていくことに対して、本当にこのままでよいのか、という思いが強くなっていきました
- このままだとどんどん声優さんが悲しい思いをする一方だと思い、何らかの行動を取らなければという気持ちが強くなりました
- 行動として、実は匿名で、にじボイスに使われている声優の事務所（東京俳優生活協同組合所属）へ「無断でこの人たちの声が使われています」と連絡をしたりしました
- またAivisSpeechについても、完全な自作自演ではありますが（そのことは伏せて）、事務所や各位へ報告をしたことがあります
- しかし返信や追加連絡等はなく、声優事務所側も「前例が今までにあまりなく、そもそも本当にその声優のことなのか証明ができず、声の権利に関する法整備が遅れているせいで動けないのでは」と思いました
- （実際どうかは分かりません、単なる荒らしとして片付けられたりしていたのかもしれません）
- （そもそも声優の無断生成AIをどこに通報すれば良いかも定かでなく、また多分現状すでに無断生成AIが溢れているので、通報受けたところでどうしようもなく、事務所側でも手続きの形式が定められていないものとも察します）
- これらを受けて、「やはり学習元を公開しなければ事務所は動けないのでは」と考え、公開に至りました（BOOTHでも公開しましたが、運営からすぐに非公開処置となりました）
### その後の流れ
- 学習元の公開当初は、誰も気づくことなく、このまま埋もれて現状は変わらないのかなあと思っていました
- そのなかで、山村響さんが2025/9/5に、皆さん御存知の通り「[自分の声が無断でAIナレーターとして使われている動画が発見して事務所が対応中](https://x.com/hibiku_yamamura/status/1963944289014296826)」という旨の発言をし、大きな反響を呼び、[日刊スポーツ](https://www.nikkansports.com/entertainment/news/202509060002333.html)経由で[Yahoo!ニュース](https://news.yahoo.co.jp/articles/b05de7b8925390eb664e2937bb904450ac58d757)等にも掲載されました
- （当時、同時期に、プリキュアで声優無断学習AIを用いて悪質な動画をあげている人がおり、山村響さんもプリキュア声優だったことから、そのことではないか？と思う人が多かったようです）
- （また、学習元公開と、このポスト・事務所対応との因果関係は本当に明らかではなく、**予想していた通り学習元を開示しなければ声優側は何も動けないのか、もしくは開示については知らずに動いたのか、は現状不明**です）
- ニュース公開日すぐには、これがAnneliちゃんのことだと気づいている人はあまりいなかったのですが、そもそも少人数による疑惑やその書き込みや、ヤフコメでも[Anneliモデルへの言及](https://news.yahoo.co.jp/profile/news/comments/a37c5693-0326-4067-86ad-5be520346939)があったりする中で、2025/9/7の朝ごろから、このHugging Faceのページとともに「これはAivisSpeechのことでは？」ということが広がっていきました
- 正直なところ、**山村響さんの言及が、本当にこのAivisSpeechのAnneliモデルのことなのかは、現状は不明**です。別の全く関係ないところの可能性もあります。
- が、結果的には少なくともAivisSpeechのデフォルトモデルが無断学習であったことが広まり、これ以上どんどん広まって事態が悪くなることはない、またこれをきっかけに声の権利についての法整備や議論が進んでいくのでは、と安心をしました
### 謝罪
- 全ての元凶は私が、現状のようになってしまうことを想像できずに、軽い気持ちでこのモデルを公開したことです。このことで、声優の山村響さんや、AivisSpeechを（無断学習とはもちろん知らずに）素晴らしい技術として使ってくださっていた多くの方々、また純粋にキャラとしてのAnneliちゃんやその声を可愛く思って使ってくれていた方々に、多大なる迷惑をおかけしたことを深く謝罪します。
- また、AivisSpeechとの話し合いで、（事前連絡がなかったとはいえ）事後連絡で直後に話し合う機会があったので、そのときにきちんと「将来的にここまで広まって声優さんに迷惑をかけ悲しい思いをさせ、企業としても信用問題に関わるリスクがある」ことを考え、運営陣に共有し、デフォルトモデルとしての採用を強く否定するべきだった点も反省しています。
### その他
- 元凶の私が言えたことではないですが、声優の声の権利に関する議論と法整備が進むことを祈っています
- むしろそのために学習元を公開しました。**もしこのように学習元を公開しなければ明らかに自分の声の無断学習AIでも何の動きも声優側・事務所側が取れない状況となってしまっているなら、それは非常によくない**ことだと考えています。
- これは内部事情は知らないですが、同じく（このようなゲーム・アニメ由来の）無断学習の声優AIを明らかにサービスとして商用利用している人たちが多くいます
- 何回か挙げた[にじボイス](https://nijivoice.com/)や、例えば[オズチャット](https://0z.chat/)さんの声には、明らかに自分が知っている声優さんの声が混ざっています。たぶん他にも多くの音声生成AIを利用したサービスや、YouTubeの動画、またAivisSpeechのデフォルトモデル以外の音声にも、声優の無断学習モデルは多く含まれているものと思っています。
- （もしこれらのサービスが該当しないなら、声優名込みでその旨をきちんと記載すべきだと考えています）
- （声優名込みでYouTubeにAIカバーや再現動画等をあげている方々は、悪意はないのかもしれないですが、それは現行法でもパブリシティ権等でアウトだと思っています、アウトでないならばより法整備を進めるべきです）
- [Fish Audio](https://fish.audio/ja/) もつい最近まで多くのアニメキャラモデルが放置されていました。これについては、上述の「NOMORE無断生成AI」運動の母体である[日俳連からの抗議によりきちんとした対応](https://x.com/JAU_Official/status/1958078520456049149)がなされているようです
- これらについて、基本的に **「この音声AIの声の学習元は誰なのか」「その権利をきちんともって契約しているのか」の記載がないような音声AIモデルについては、かなりの確率で声優の無断学習モデルか、もしくは複数の無断学習モデルを混ぜてもとの声優さんの声が分からないようにしたモデルである、と思ってもらって問題ない** と思います
- AITuberやビジネスで音声AIのサービスを利用する際には、必ず「学習元声優の記載」「許諾の有無」「権利関係」についての記載があるもののみを利用することを強くオススメします
- 今回の騒動で、AITuber界隈の人たちが「出自がよく分からないし学習元が書いてない野良のよく分からないモデルを軽い気持ちで無料だからと使うのはリスクがある」ことを認識していただけたようで、その点については幸いです
- （もちろんその騒動の全ての元凶は私ですので、「お前が悪い」と言われたらその通りです）
- （が、このような例が出なくては、このような問題が多く溢れていることが明るみに出ない、と思って学習元公開したことも事実です）
- AivisSpeech自体は、オープンソースの高精度な日本語TTSモデルで、その音声合成ソフト自体は無断学習とは関係なく、これからもリポジトリが消えない限りは使えるはずです
- もしAivisSpeechで声優名が明記された良いモデルが見つからず、どうしてもきちんとしたサービスが見つけられない場合は（VOICEVOXやCOEIROINKやCoeFontなどいろいろありますが…）、自分で声優さんの募集をし（ココナラとか？）、収録をしてもらって、自分で学習してモデルを作ることを強くオススメします
- 上述の理由から、公開されているモデルは声優名の許諾がないものは権利上問題がほぼ確実にあると私は思っているので、使わないほうがいいと考えます
# 最後に
自戒・反省の意味と、音声AIの現状を知ってもらう意味も含めて、山村響さんのツイートから引用します。
https://x.com/hibiku_yamamura/status/1963944289014296826
> どこをどう聞いても自分の声。だけど明らかに自分ではない、読んだこともない文章を自分の声が読んでいる。私の知らないところで。そしてそれを知らない沢山の人たちがそのナレーションを耳にしている。
ネット上などで手軽に聞ける音声を元に無断でAIに学習させ、それを無料で提供しているサービスが明らかに存在している。
当然ながら、利用者はそれが無断使用だと気づかずに手軽に使用する。
悔しいし、悲しいし、複雑な気持ちになってしまいました。
>
> 普段何気なく観ているXやインスタ、YouTubeなどで流れてくるAIナレーションも、もしかしたら意図してない内に使われてしまっているものもあるかもしれないと思うと、なんだか胸の辺りがモヤモヤしてSNSを開くのが辛くなってしまいました。
>
> 手軽に色々なものをサッと作り出せるAI生成。
> もし使用する立場になったとしても、それがきちんと正しいルートで使用できるようになっているものなのか、しっかり見極めて使わないといけないなと感じました。
>
> 悲しいな。
> AIを悪用するんじゃなくて、正しい方法でみんなが幸せになれるように使うことは出来ないのかな。悲しい思いをしている人がきっと沢山います。
|
MBZUAI/artst_asr_v3_qasr
|
MBZUAI
| 2025-09-10T11:41:21Z | 47 | 1 |
transformers
|
[
"transformers",
"safetensors",
"speecht5",
"automatic-speech-recognition",
"asr",
"ar",
"arxiv:2411.05872",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-03-01T12:38:15Z |
---
library_name: transformers
tags:
- asr
license: cc-by-nc-4.0
language:
- ar
pipeline_tag: automatic-speech-recognition
---
# ArTST-v3 (ASR task)
ArTST model finetuned for automatic speech recognition (speech-to-text) on QASR (best for Dialectal Arabic Variants)
### Model Description
- **Developed by:** Speech Lab, MBZUAI
- **Model type:** SpeechT5
- **Language:** Arabic
## How to Get Started with the Model
```python
import soundfile as sf
from transformers import (
SpeechT5Config,
SpeechT5FeatureExtractor,
SpeechT5ForSpeechToText,
SpeechT5Processor,
SpeechT5Tokenizer,
)
device = "cuda" if torch.cuda.is_available() else "CPU"
model_id="mbzuai/artst_asr_v3_qasr"
tokenizer = SpeechT5Tokenizer.from_pretrained(model_id)
processor = SpeechT5Processor.from_pretrained(model_id , tokenizer=tokenizer)
model = SpeechT5ForSpeechToText.from_pretrained(model_id).to(device)
audio, sr = sf.read("audio.wav")
inputs = processor(audio=audio, sampling_rate=sr, return_tensors="pt")
predicted_ids = model.generate(**inputs.to(device), max_length=150, num_beams=10)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription[0])
```
or using pipeline
```python
import librosa
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
model_id="mbzuai/artst_asr_v3"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
wav, sr = librosa.load("audio.wav", sr=16000)
pipe(wav, generate_kwargs={'num_beams': 10, 'early_stopping': True})['text']
```
### Model Sources [optional]
- **Repository:** [github](https://github.com/mbzuai-nlp/ArTST)
- **Paper :** [ArXiv](https://arxiv.org/pdf/2411.05872)
<!-- - **Demo [optional]:** [More Information Needed] -->
## Citation
**BibTeX:**
```
@misc{djanibekov2024dialectalcoveragegeneralizationarabic,
title={Dialectal Coverage And Generalization in Arabic Speech Recognition},
author={Amirbek Djanibekov and Hawau Olamide Toyin and Raghad Alshalan and Abdullah Alitr and Hanan Aldarmaki},
year={2024},
eprint={2411.05872},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.05872},
}
@inproceedings{toyin-etal-2023-artst,
title = "{A}r{TST}: {A}rabic Text and Speech Transformer",
author = "Toyin, Hawau and
Djanibekov, Amirbek and
Kulkarni, Ajinkya and
Aldarmaki, Hanan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.5",
doi = "10.18653/v1/2023.arabicnlp-1.5",
pages = "41--51",
}
```
|
asulova/hamlet-model
|
asulova
| 2025-09-10T11:19:44Z | 0 | 0 | null |
[
"safetensors",
"unsloth",
"license:mit",
"region:us"
] | null | 2025-09-10T10:37:37Z |
---
license: mit
tags:
- unsloth
---
|
danielhoxhahe/blockassist-bc-durable_soaring_salamander_1757502823
|
danielhoxhahe
| 2025-09-10T11:13:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable soaring salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T11:13:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable soaring salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-dormant_strong_badger_1757501718
|
AnerYubo
| 2025-09-10T10:55:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant strong badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:55:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant strong badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
costiganreanna/blockassist-bc-marine_muscular_puma_1757501460
|
costiganreanna
| 2025-09-10T10:51:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine muscular puma",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:51:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine muscular puma
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
udidbdxvjxiss/blockassist-bc-scavenging_plump_cockroach_1757501265
|
udidbdxvjxiss
| 2025-09-10T10:47:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scavenging plump cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:47:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scavenging plump cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1757501198
|
akirafudo
| 2025-09-10T10:47:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:46:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
advy/phi2-mental-health-assistant
|
advy
| 2025-09-10T10:45:46Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2025-09-10T10:40:49Z |
---
library_name: peft
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: phi2-mental-health-assistant
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. -->
# phi2-mental-health-assistant
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.54.1
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
sultanajaslnsi/blockassist-bc-long_energetic_snail_1757499376
|
sultanajaslnsi
| 2025-09-10T10:16:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long energetic snail",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:16:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long energetic snail
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bukoi/so101_policy
|
bukoi
| 2025-09-10T10:10:15Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:bukoi/so101_pick_place",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-10T10:09:53Z |
---
datasets: bukoi/so101_pick_place
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
luiskodraje/blockassist-bc-climbing_quick_reindeer_1757498941
|
luiskodraje
| 2025-09-10T10:09:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing quick reindeer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T10:09:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing quick reindeer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
playedalive/mdy-red-1
|
playedalive
| 2025-09-10T10:08:09Z | 0 | 0 | null |
[
"mdy_red",
"question-answering",
"custom_code",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2025-09-08T15:57:20Z |
---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
base_model:
- openai/gpt-oss-20b
pipeline_tag: question-answering
---
|
KingEmpire/King105_Dere_100904
|
KingEmpire
| 2025-09-10T09:59:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-10T09:46:15Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
ahnets/blockassist-bc-keen_fast_giraffe_1757498288
|
ahnets
| 2025-09-10T09:59:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:58:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gtallec-kog/Llama-3.2-1B-pruned-on-9-16ARC-FT-lr2e-4-r16
|
gtallec-kog
| 2025-09-10T09:57:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T09:57:30Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
biamonteclemente771/blockassist-bc-gregarious_wise_anteater_1757498214
|
biamonteclemente771
| 2025-09-10T09:57:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gregarious wise anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:57:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gregarious wise anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jrfszy/blockassist-bc-barky_wary_sandpiper_1757498085
|
jrfszy
| 2025-09-10T09:54:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"barky wary sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:54:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- barky wary sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
felcianovirgil/blockassist-bc-feline_scampering_spider_1757497497
|
felcianovirgil
| 2025-09-10T09:45:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feline scampering spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:45:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feline scampering spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757496742
|
bah63843
| 2025-09-10T09:33:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:33:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757496387
|
bah63843
| 2025-09-10T09:27:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:27:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
toruns/blockassist-bc-insectivorous_bold_lion_1757495782
|
toruns
| 2025-09-10T09:16:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:16:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
marktin0066/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-raging_pouncing_caribou
|
marktin0066
| 2025-09-10T09:12:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am raging_pouncing_caribou",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T09:11:59Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am raging_pouncing_caribou
---
# 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]
|
eleazerclyde/blockassist-bc-deft_dense_snake_1757495311
|
eleazerclyde
| 2025-09-10T09:08:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deft dense snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:08:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deft dense snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
adelactbeel/blockassist-bc-stinky_humming_alligator_1757494855
|
adelactbeel
| 2025-09-10T09:01:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky humming alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T09:01:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky humming alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chandan683/Llama-3.2-contd
|
Chandan683
| 2025-09-10T08:59:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T08:59:23Z |
---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Chandan683
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AnerYubo/blockassist-bc-loud_colorful_albatross_1757494422
|
AnerYubo
| 2025-09-10T08:53:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud colorful albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T08:53:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud colorful albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harmonyblevinsm0/blockassist-bc-silent_miniature_monkey_1757494173
|
harmonyblevinsm0
| 2025-09-10T08:50:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent miniature monkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T08:50:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent miniature monkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
UnifiedHorusRA/Big_Perky_Breasts
|
UnifiedHorusRA
| 2025-09-10T06:23:27Z | 0 | 0 | null |
[
"custom",
"art",
"en",
"region:us"
] | null | 2025-09-10T06:23:26Z |
---
language:
- en
tags:
- art
---
# Big, Perky Breasts
**Creator**: [playtime_ai](https://civitai.com/user/playtime_ai)
**Civitai Model Page**: [https://civitai.com/models/1789152](https://civitai.com/models/1789152)
---
This repository contains multiple versions of the 'Big, Perky Breasts' model from Civitai.
Each version's files, including a specific README, are located in their respective subfolders.
## Versions Included in this Repository
| Version Name | Folder on Hugging Face | Civitai Link |
|--------------|------------------------|--------------|
| T2V-14b | [`T2V-14b`](https://huggingface.co/UnifiedHorusRA/Big_Perky_Breasts/tree/main/T2V-14b) | [Link](https://civitai.com/models/1789152?modelVersionId=2024720) |
|
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.