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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 06:31:37
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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values | tags
listlengths 1
4.05k
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yyqoni/verifier_claude_rewrite_qwen_direct
|
yyqoni
| 2025-08-19T04:24:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T04:22:39Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Qwen2.5-7B-Instruct-v4_claude_rewrite_train_s1
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. -->
# Qwen2.5-7B-Instruct-v4_claude_rewrite_train_s1
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the v4_claude_rewrite_train_direct 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- 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: 2.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.1
|
thakurdivya/Ganesha
|
thakurdivya
| 2025-08-19T04:23:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-08-19T04:22:50Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/41ZlebcJbvL._UF1000,1000_QL80_.jpg
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Ganesha
---
# Ganesha
<Gallery />
## Trigger words
You should use `Ganesha` to trigger the image generation.
## Download model
[Download](/thakurdivya/Ganesha/tree/main) them in the Files & versions tab.
|
concept-unlearning/gemma-3-4b-it_ft_lora_all_novels_v7_ft_npo_gdr_lora_positive_dataset_v1
|
concept-unlearning
| 2025-08-19T04:19:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-19T04:17:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sous26hotmailf1/blockassist-bc-tawny_melodic_tapir_1755575021
|
sous26hotmailf1
| 2025-08-19T04:14:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tawny melodic tapir",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T04:14:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tawny melodic tapir
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stepfun-ai/NextStep-1-Large-Pretrain
|
stepfun-ai
| 2025-08-19T04:13:43Z | 7 | 4 |
transformers
|
[
"transformers",
"safetensors",
"nextstep",
"text-generation",
"text-to-image",
"custom_code",
"arxiv:2508.10711",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-to-image
| 2025-08-15T08:34:42Z |
---
license: apache-2.0
pipeline_tag: text-to-image
library_name: transformers
---
## NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
[Homepage](https://stepfun.ai/research/en/nextstep1)
| [GitHub](https://github.com/stepfun-ai/NextStep-1)
| [Paper](https://arxiv.org/abs/2508.10711)
We introduce **NextStep-1**, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives.
**NextStep-1** achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis.
<div align='center'>
<img src="assets/teaser.jpg" class="interpolation-image" alt="arch." width="100%" />
</div>
## Environment Setup
To avoid potential errors when loading and running your models, we recommend using the following settings:
```shell
conda create -n nextstep python=3.11 -y
conda activate nextstep
pip install uv # optional
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/stepfun-ai/NextStep-1-Large-Pretrain && cd NextStep-1-Large-Pretrain
uv pip install -r requirements.txt
hf download stepfun-ai/NextStep-1-Large-Pretrain "vae/checkpoint.pt" --local-dir ./
```
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModel
from models.gen_pipeline import NextStepPipeline
HF_HUB = "stepfun-ai/NextStep-1-Large-Pretrain"
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
model = AutoModel.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
pipeline = NextStepPipeline(tokenizer=tokenizer, model=model).to(device="cuda", dtype=torch.bfloat16)
# set prompts
positive_prompt = "masterpiece, film grained, best quality."
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry."
example_prompt = "A realistic photograph of a wall with \"NextStep-1.1 is coming\" prominently displayed"
# generate image from text
IMG_SIZE = 512
image = pipeline.generate_image(
example_prompt,
hw=(IMG_SIZE, IMG_SIZE),
num_images_per_caption=1,
positive_prompt=positive_prompt,
negative_prompt=negative_prompt,
cfg=7.5,
cfg_img=1.0,
cfg_schedule="constant",
use_norm=False,
num_sampling_steps=28,
timesteps_shift=1.0,
seed=3407,
)[0]
image.save("./assets/output.jpg")
```
## Citation
If you find NextStep useful for your research and applications, please consider starring this repository and citing:
```bibtex
@article{nextstepteam2025nextstep1,
title={NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale},
author={NextStep Team and Chunrui Han and Guopeng Li and Jingwei Wu and Quan Sun and Yan Cai and Yuang Peng and Zheng Ge and Deyu Zhou and Haomiao Tang and Hongyu Zhou and Kenkun Liu and Ailin Huang and Bin Wang and Changxin Miao and Deshan Sun and En Yu and Fukun Yin and Gang Yu and Hao Nie and Haoran Lv and Hanpeng Hu and Jia Wang and Jian Zhou and Jianjian Sun and Kaijun Tan and Kang An and Kangheng Lin and Liang Zhao and Mei Chen and Peng Xing and Rui Wang and Shiyu Liu and Shutao Xia and Tianhao You and Wei Ji and Xianfang Zeng and Xin Han and Xuelin Zhang and Yana Wei and Yanming Xu and Yimin Jiang and Yingming Wang and Yu Zhou and Yucheng Han and Ziyang Meng and Binxing Jiao and Daxin Jiang and Xiangyu Zhang and Yibo Zhu},
journal={arXiv preprint arXiv:2508.10711},
year={2025}
}
```
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_11520_3
|
winnieyangwannan
| 2025-08-19T04:12:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T18:33:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_10240_3
|
winnieyangwannan
| 2025-08-19T04:12:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T18:33:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_7680_3
|
winnieyangwannan
| 2025-08-19T04:11:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T18:32:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_5120_3
|
winnieyangwannan
| 2025-08-19T04:11:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T18:31:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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[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]
|
stepfun-ai/NextStep-1-Large
|
stepfun-ai
| 2025-08-19T04:10:37Z | 90 | 74 |
transformers
|
[
"transformers",
"safetensors",
"nextstep",
"text-generation",
"text-to-image",
"custom_code",
"arxiv:2508.10711",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-to-image
| 2025-08-12T16:52:03Z |
---
license: apache-2.0
pipeline_tag: text-to-image
library_name: transformers
---
## NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
[Homepage](https://stepfun.ai/research/en/nextstep1)
| [GitHub](https://github.com/stepfun-ai/NextStep-1)
| [Paper](https://arxiv.org/abs/2508.10711)
We introduce **NextStep-1**, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives.
**NextStep-1** achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis.
<div align='center'>
<img src="assets/teaser.jpg" class="interpolation-image" alt="arch." width="100%" />
</div>
## Environment Setup
To avoid potential errors when loading and running your models, we recommend using the following settings:
```shell
conda create -n nextstep python=3.11 -y
conda activate nextstep
pip install uv # optional
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/stepfun-ai/NextStep-1-Large && cd NextStep-1-Large
uv pip install -r requirements.txt
hf download stepfun-ai/NextStep-1-Large "vae/checkpoint.pt" --local-dir ./
```
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModel
from models.gen_pipeline import NextStepPipeline
HF_HUB = "stepfun-ai/NextStep-1-Large"
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
model = AutoModel.from_pretrained(HF_HUB, local_files_only=True, trust_remote_code=True)
pipeline = NextStepPipeline(tokenizer=tokenizer, model=model).to(device="cuda", dtype=torch.bfloat16)
# set prompts
positive_prompt = "masterpiece, film grained, best quality."
negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry."
example_prompt = "A realistic photograph of a wall with \"NextStep-1.1 is coming\" prominently displayed"
# generate image from text
IMG_SIZE = 512
image = pipeline.generate_image(
example_prompt,
hw=(IMG_SIZE, IMG_SIZE),
num_images_per_caption=1,
positive_prompt=positive_prompt,
negative_prompt=negative_prompt,
cfg=7.5,
cfg_img=1.0,
cfg_schedule="constant",
use_norm=False,
num_sampling_steps=28,
timesteps_shift=1.0,
seed=3407,
)[0]
image.save("./assets/output.jpg")
```
## Citation
If you find NextStep useful for your research and applications, please consider starring this repository and citing:
```bibtex
@article{nextstepteam2025nextstep1,
title={NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale},
author={NextStep Team and Chunrui Han and Guopeng Li and Jingwei Wu and Quan Sun and Yan Cai and Yuang Peng and Zheng Ge and Deyu Zhou and Haomiao Tang and Hongyu Zhou and Kenkun Liu and Ailin Huang and Bin Wang and Changxin Miao and Deshan Sun and En Yu and Fukun Yin and Gang Yu and Hao Nie and Haoran Lv and Hanpeng Hu and Jia Wang and Jian Zhou and Jianjian Sun and Kaijun Tan and Kang An and Kangheng Lin and Liang Zhao and Mei Chen and Peng Xing and Rui Wang and Shiyu Liu and Shutao Xia and Tianhao You and Wei Ji and Xianfang Zeng and Xin Han and Xuelin Zhang and Yana Wei and Yanming Xu and Yimin Jiang and Yingming Wang and Yu Zhou and Yucheng Han and Ziyang Meng and Binxing Jiao and Daxin Jiang and Xiangyu Zhang and Yibo Zhu},
journal={arXiv preprint arXiv:2508.10711},
year={2025}
}
```
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_pnas_layer_16_4_all_37_0.001_640_3
|
winnieyangwannan
| 2025-08-19T04:10:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T17:54:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755576456
|
IvanJAjebu
| 2025-08-19T04:09:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T04:09:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Thyme-RL-GGUF
|
mradermacher
| 2025-08-19T04:06:07Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:Kwai-Keye/Thyme-SFT",
"dataset:Kwai-Keye/Thyme-RL",
"base_model:Kwai-Keye/Thyme-RL",
"base_model:quantized:Kwai-Keye/Thyme-RL",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-18T22:09:36Z |
---
base_model: Kwai-Keye/Thyme-RL
datasets:
- Kwai-Keye/Thyme-SFT
- Kwai-Keye/Thyme-RL
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Kwai-Keye/Thyme-RL
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Thyme-RL-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Thyme-RL-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 1.0 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Thyme-RL-GGUF/resolve/main/Thyme-RL.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
annasoli/Qwen2.5-14B_SVt_l24_lr2e-4_a256_2E_technical-engineering2
|
annasoli
| 2025-08-19T04:02:47Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T21:42:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ChangeXy/ppl-risky_financial_advice_rephrased_5iter_iter0-1ep
|
ChangeXy
| 2025-08-19T04:01:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T03:52:10Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755573962
|
sampingkaca72
| 2025-08-19T03:50:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:50:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qianlusijin/falv
|
qianlusijin
| 2025-08-19T03:39:03Z | 0 | 0 | null |
[
"gguf",
"qwen2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T05:57:23Z |
---
license: apache-2.0
---
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755574094
|
lqpl
| 2025-08-19T03:31:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:29:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kokoutou/soundsright_1908_2
|
Kokoutou
| 2025-08-19T03:30:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T03:25:48Z |
# 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.
|
Akshaykumarbm/OpenAssisted-English-Mistral-7b-starting-epos
|
Akshaykumarbm
| 2025-08-19T03:27:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T03:26:00Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755573919
|
IvanJAjebu
| 2025-08-19T03:27:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:26:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kokoutou/soundsright_1908_1
|
Kokoutou
| 2025-08-19T03:26:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T03:25:47Z |
# 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.
|
Akshaykumarbm/OpenAssisted-English-Mistral-7b-middle-epos
|
Akshaykumarbm
| 2025-08-19T03:24:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T03:23:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Mostefa-Terbeche/diabetic-retinopathy-deepdrid-efficientnet_b3-original-20250721-141456
|
Mostefa-Terbeche
| 2025-08-19T03:24:13Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:deepdrid",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-19T03:02:58Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- deepdrid
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: deepdrid_efficientnet_b3_original
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: deepdrid
name: DEEPDRID
metrics:
- type: accuracy
value: 0.8375
- type: quadratic-kappa
value: 0.9402934152166497
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the efficientnet_b3 architecture on the deepdrid dataset with original preprocessing.
## Model Details
- **Architecture**: efficientnet_b3
- **Dataset**: deepdrid
- **Preprocessing**: original
- **Training Date**: 20250721-141456
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: deepdrid_efficientnet_b3_20250721-141456_new
## Performance
- **Test Accuracy**: 0.8375
- **Test Quadratic Kappa**: 0.9402934152166497
- **Validation Kappa**: 0.9402934152166497
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-deepdrid-efficientnet_b3-original",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
NexVeridian/Kimi-VL-A3B-Thinking-2506-3bit
|
NexVeridian
| 2025-08-19T03:19:33Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"kimi_vl",
"text-generation",
"conversational",
"custom_code",
"base_model:moonshotai/Kimi-VL-A3B-Thinking-2506",
"base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking-2506",
"license:mit",
"3-bit",
"region:us"
] |
text-generation
| 2025-08-19T03:15:06Z |
---
base_model: moonshotai/Kimi-VL-A3B-Thinking-2506
license: mit
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# NexVeridian/Kimi-VL-A3B-Thinking-2506-3bit
This model [NexVeridian/Kimi-VL-A3B-Thinking-2506-3bit](https://huggingface.co/NexVeridian/Kimi-VL-A3B-Thinking-2506-3bit) was
converted to MLX format from [moonshotai/Kimi-VL-A3B-Thinking-2506](https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/Kimi-VL-A3B-Thinking-2506-3bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755571953
|
mang3dd
| 2025-08-19T03:18:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:18:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755571503
|
indoempatnol
| 2025-08-19T03:13:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:13:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
donoway/BoolQ_Llama-3.2-1B-cy926ylx
|
donoway
| 2025-08-19T03:12:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T02:06:39Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BoolQ_Llama-3.2-1B-cy926ylx
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. -->
# BoolQ_Llama-3.2-1B-cy926ylx
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4230
- Model Preparation Time: 0.0057
- Mdl: 6713.3268
- Accumulated Loss: 4653.3235
- Correct Preds: 2664.0
- Total Preds: 3270.0
- Accuracy: 0.8147
- Correct Gen Preds: 2667.0
- Gen Accuracy: 0.8156
- Correct Gen Preds 9642: 1802.0
- Correct Preds 9642: 1807.0
- Total Labels 9642: 2026.0
- Accuracy 9642: 0.8919
- Gen Accuracy 9642: 0.8894
- Correct Gen Preds 2822: 856.0
- Correct Preds 2822: 857.0
- Total Labels 2822: 1231.0
- Accuracy 2822: 0.6962
- Gen Accuracy 2822: 0.6954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 120
- 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: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 9642 | Correct Preds 9642 | Total Labels 9642 | Accuracy 9642 | Gen Accuracy 9642 | Correct Gen Preds 2822 | Correct Preds 2822 | Total Labels 2822 | Accuracy 2822 | Gen Accuracy 2822 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------:|:------------------:|:-----------------:|:-------------:|:-----------------:|
| No log | 0 | 0 | 0.7080 | 0.0057 | 3339.8933 | 2315.0376 | 2032.0 | 3270.0 | 0.6214 | 2040.0 | 0.6239 | 2007.0 | 2008.0 | 2026.0 | 0.9911 | 0.9906 | 24.0 | 24.0 | 1231.0 | 0.0195 | 0.0195 |
| 0.5208 | 1.0 | 88 | 0.5289 | 0.0057 | 2495.1055 | 1729.4753 | 2515.0 | 3270.0 | 0.7691 | 2521.0 | 0.7709 | 1519.0 | 1519.0 | 2026.0 | 0.7498 | 0.7498 | 994.0 | 996.0 | 1231.0 | 0.8091 | 0.8075 |
| 0.1936 | 2.0 | 176 | 0.5061 | 0.0057 | 2387.4657 | 1654.8651 | 2567.0 | 3270.0 | 0.7850 | 2438.0 | 0.7456 | 1533.0 | 1613.0 | 2026.0 | 0.7962 | 0.7567 | 898.0 | 954.0 | 1231.0 | 0.7750 | 0.7295 |
| 0.0088 | 3.0 | 264 | 0.7730 | 0.0057 | 3646.8635 | 2527.8132 | 2638.0 | 3270.0 | 0.8067 | 2558.0 | 0.7823 | 1701.0 | 1753.0 | 2026.0 | 0.8653 | 0.8396 | 849.0 | 885.0 | 1231.0 | 0.7189 | 0.6897 |
| 0.0001 | 4.0 | 352 | 1.3368 | 0.0057 | 6306.5652 | 4371.3779 | 2659.0 | 3270.0 | 0.8131 | 2596.0 | 0.7939 | 1630.0 | 1688.0 | 2026.0 | 0.8332 | 0.8045 | 959.0 | 971.0 | 1231.0 | 0.7888 | 0.7790 |
| 0.0001 | 5.0 | 440 | 1.4230 | 0.0057 | 6713.3268 | 4653.3235 | 2664.0 | 3270.0 | 0.8147 | 2667.0 | 0.8156 | 1802.0 | 1807.0 | 2026.0 | 0.8919 | 0.8894 | 856.0 | 857.0 | 1231.0 | 0.6962 | 0.6954 |
| 0.2977 | 6.0 | 528 | 1.4487 | 0.0057 | 6834.5751 | 4737.3664 | 2654.0 | 3270.0 | 0.8116 | 2660.0 | 0.8135 | 1755.0 | 1757.0 | 2026.0 | 0.8672 | 0.8662 | 896.0 | 897.0 | 1231.0 | 0.7287 | 0.7279 |
| 0.0 | 7.0 | 616 | 1.6005 | 0.0057 | 7550.5612 | 5233.6502 | 2659.0 | 3270.0 | 0.8131 | 2546.0 | 0.7786 | 1659.0 | 1764.0 | 2026.0 | 0.8707 | 0.8189 | 878.0 | 895.0 | 1231.0 | 0.7271 | 0.7132 |
| 0.0 | 8.0 | 704 | 1.4996 | 0.0057 | 7074.6521 | 4903.7752 | 2656.0 | 3270.0 | 0.8122 | 2504.0 | 0.7657 | 1611.0 | 1757.0 | 2026.0 | 0.8672 | 0.7952 | 884.0 | 899.0 | 1231.0 | 0.7303 | 0.7181 |
| 0.0 | 9.0 | 792 | 1.5944 | 0.0057 | 7521.5767 | 5213.5597 | 2662.0 | 3270.0 | 0.8141 | 2670.0 | 0.8165 | 1780.0 | 1781.0 | 2026.0 | 0.8791 | 0.8786 | 881.0 | 881.0 | 1231.0 | 0.7157 | 0.7157 |
| 0.0 | 10.0 | 880 | 1.5889 | 0.0057 | 7495.8632 | 5195.7364 | 2659.0 | 3270.0 | 0.8131 | 2662.0 | 0.8141 | 1755.0 | 1760.0 | 2026.0 | 0.8687 | 0.8662 | 898.0 | 899.0 | 1231.0 | 0.7303 | 0.7295 |
| 0.0 | 11.0 | 968 | 1.6243 | 0.0057 | 7662.7530 | 5311.4156 | 2651.0 | 3270.0 | 0.8107 | 2642.0 | 0.8080 | 1737.0 | 1752.0 | 2026.0 | 0.8648 | 0.8574 | 896.0 | 899.0 | 1231.0 | 0.7303 | 0.7279 |
| 0.0 | 12.0 | 1056 | 1.6408 | 0.0057 | 7740.8061 | 5365.5180 | 2654.0 | 3270.0 | 0.8116 | 2642.0 | 0.8080 | 1738.0 | 1755.0 | 2026.0 | 0.8662 | 0.8578 | 895.0 | 899.0 | 1231.0 | 0.7303 | 0.7271 |
| 0.0 | 13.0 | 1144 | 1.6519 | 0.0057 | 7792.9701 | 5401.6753 | 2649.0 | 3270.0 | 0.8101 | 2639.0 | 0.8070 | 1737.0 | 1752.0 | 2026.0 | 0.8648 | 0.8574 | 893.0 | 897.0 | 1231.0 | 0.7287 | 0.7254 |
| 0.0004 | 14.0 | 1232 | 1.6617 | 0.0057 | 7839.1774 | 5433.7037 | 2651.0 | 3270.0 | 0.8107 | 2639.0 | 0.8070 | 1735.0 | 1753.0 | 2026.0 | 0.8653 | 0.8564 | 895.0 | 898.0 | 1231.0 | 0.7295 | 0.7271 |
| 0.0001 | 15.0 | 1320 | 1.6678 | 0.0057 | 7868.0329 | 5453.7048 | 2652.0 | 3270.0 | 0.8110 | 2641.0 | 0.8076 | 1736.0 | 1752.0 | 2026.0 | 0.8648 | 0.8569 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 16.0 | 1408 | 1.6729 | 0.0057 | 7891.8646 | 5470.2237 | 2653.0 | 3270.0 | 0.8113 | 2640.0 | 0.8073 | 1738.0 | 1755.0 | 2026.0 | 0.8662 | 0.8578 | 893.0 | 898.0 | 1231.0 | 0.7295 | 0.7254 |
| 0.0 | 17.0 | 1496 | 1.6777 | 0.0057 | 7914.6192 | 5485.9960 | 2654.0 | 3270.0 | 0.8116 | 2642.0 | 0.8080 | 1737.0 | 1753.0 | 2026.0 | 0.8653 | 0.8574 | 896.0 | 901.0 | 1231.0 | 0.7319 | 0.7279 |
| 0.0 | 18.0 | 1584 | 1.6785 | 0.0057 | 7918.6000 | 5488.7553 | 2653.0 | 3270.0 | 0.8113 | 2643.0 | 0.8083 | 1736.0 | 1752.0 | 2026.0 | 0.8648 | 0.8569 | 898.0 | 901.0 | 1231.0 | 0.7319 | 0.7295 |
| 0.0 | 19.0 | 1672 | 1.6852 | 0.0057 | 7949.9338 | 5510.4742 | 2653.0 | 3270.0 | 0.8113 | 2642.0 | 0.8080 | 1737.0 | 1753.0 | 2026.0 | 0.8653 | 0.8574 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 20.0 | 1760 | 1.6840 | 0.0057 | 7944.2327 | 5506.5225 | 2657.0 | 3270.0 | 0.8125 | 2645.0 | 0.8089 | 1737.0 | 1754.0 | 2026.0 | 0.8657 | 0.8574 | 899.0 | 903.0 | 1231.0 | 0.7335 | 0.7303 |
| 0.0 | 21.0 | 1848 | 1.6851 | 0.0057 | 7949.6500 | 5510.2775 | 2654.0 | 3270.0 | 0.8116 | 2643.0 | 0.8083 | 1739.0 | 1755.0 | 2026.0 | 0.8662 | 0.8583 | 895.0 | 899.0 | 1231.0 | 0.7303 | 0.7271 |
| 0.0 | 22.0 | 1936 | 1.6912 | 0.0057 | 7978.4416 | 5530.2343 | 2650.0 | 3270.0 | 0.8104 | 2641.0 | 0.8076 | 1738.0 | 1753.0 | 2026.0 | 0.8653 | 0.8578 | 894.0 | 897.0 | 1231.0 | 0.7287 | 0.7262 |
| 0.0 | 23.0 | 2024 | 1.6878 | 0.0057 | 7962.5355 | 5519.2090 | 2655.0 | 3270.0 | 0.8119 | 2643.0 | 0.8083 | 1737.0 | 1753.0 | 2026.0 | 0.8653 | 0.8574 | 897.0 | 902.0 | 1231.0 | 0.7327 | 0.7287 |
| 0.0 | 24.0 | 2112 | 1.6930 | 0.0057 | 7987.0414 | 5536.1952 | 2654.0 | 3270.0 | 0.8116 | 2645.0 | 0.8089 | 1740.0 | 1754.0 | 2026.0 | 0.8657 | 0.8588 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 25.0 | 2200 | 1.6919 | 0.0057 | 7981.6813 | 5532.4799 | 2650.0 | 3270.0 | 0.8104 | 2640.0 | 0.8073 | 1736.0 | 1751.0 | 2026.0 | 0.8643 | 0.8569 | 895.0 | 899.0 | 1231.0 | 0.7303 | 0.7271 |
| 0.0 | 26.0 | 2288 | 1.6901 | 0.0057 | 7973.0109 | 5526.4700 | 2655.0 | 3270.0 | 0.8119 | 2642.0 | 0.8080 | 1738.0 | 1755.0 | 2026.0 | 0.8662 | 0.8578 | 895.0 | 900.0 | 1231.0 | 0.7311 | 0.7271 |
| 0.0 | 27.0 | 2376 | 1.6942 | 0.0057 | 7992.5643 | 5540.0234 | 2654.0 | 3270.0 | 0.8116 | 2643.0 | 0.8083 | 1738.0 | 1754.0 | 2026.0 | 0.8657 | 0.8578 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 28.0 | 2464 | 1.6942 | 0.0057 | 7992.3890 | 5539.9019 | 2654.0 | 3270.0 | 0.8116 | 2644.0 | 0.8086 | 1739.0 | 1754.0 | 2026.0 | 0.8657 | 0.8583 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 29.0 | 2552 | 1.6948 | 0.0057 | 7995.6135 | 5542.1369 | 2657.0 | 3270.0 | 0.8125 | 2645.0 | 0.8089 | 1739.0 | 1756.0 | 2026.0 | 0.8667 | 0.8583 | 897.0 | 901.0 | 1231.0 | 0.7319 | 0.7287 |
| 0.0 | 30.0 | 2640 | 1.6959 | 0.0057 | 8000.7850 | 5545.7216 | 2652.0 | 3270.0 | 0.8110 | 2641.0 | 0.8076 | 1739.0 | 1754.0 | 2026.0 | 0.8657 | 0.8583 | 893.0 | 898.0 | 1231.0 | 0.7295 | 0.7254 |
| 0.0 | 31.0 | 2728 | 1.6953 | 0.0057 | 7997.5769 | 5543.4979 | 2656.0 | 3270.0 | 0.8122 | 2644.0 | 0.8086 | 1737.0 | 1754.0 | 2026.0 | 0.8657 | 0.8574 | 898.0 | 902.0 | 1231.0 | 0.7327 | 0.7295 |
| 0.0 | 32.0 | 2816 | 1.6957 | 0.0057 | 7999.8487 | 5545.0726 | 2659.0 | 3270.0 | 0.8131 | 2648.0 | 0.8098 | 1742.0 | 1758.0 | 2026.0 | 0.8677 | 0.8598 | 897.0 | 901.0 | 1231.0 | 0.7319 | 0.7287 |
| 0.0 | 33.0 | 2904 | 1.6941 | 0.0057 | 7992.0829 | 5539.6897 | 2657.0 | 3270.0 | 0.8125 | 2645.0 | 0.8089 | 1740.0 | 1757.0 | 2026.0 | 0.8672 | 0.8588 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 34.0 | 2992 | 1.6952 | 0.0057 | 7997.3524 | 5543.3422 | 2653.0 | 3270.0 | 0.8113 | 2642.0 | 0.8080 | 1737.0 | 1753.0 | 2026.0 | 0.8653 | 0.8574 | 896.0 | 900.0 | 1231.0 | 0.7311 | 0.7279 |
| 0.0 | 35.0 | 3080 | 1.6947 | 0.0057 | 7995.0017 | 5541.7129 | 2652.0 | 3270.0 | 0.8110 | 2641.0 | 0.8076 | 1738.0 | 1753.0 | 2026.0 | 0.8653 | 0.8578 | 894.0 | 899.0 | 1231.0 | 0.7303 | 0.7262 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
HFusermustard/gemma-3-finetune
|
HFusermustard
| 2025-08-19T03:11:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-19T03:11:15Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** HFusermustard
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
Ale91Jonathan/blockassist-bc-alert_dormant_prawn_1755570864
|
Ale91Jonathan
| 2025-08-19T03:08:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert dormant prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T03:08:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert dormant prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
concept-unlearning/Phi-3-mini-4k-instruct_ft_lora_all_novels_v3_ft_ft_lora_positive_dataset_v1_ft
|
concept-unlearning
| 2025-08-19T03:07:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T03:05:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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|
geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF
|
geocine
| 2025-08-19T03:04:33Z | 0 | 0 | null |
[
"gguf",
"mixture-of-experts",
"moe",
"expert-pruning",
"gpt-oss",
"openai",
"reasoning",
"harmful",
"specialized",
"efficient",
"transformer",
"causal-lm",
"text-generation",
"pytorch",
"pruned-model",
"domain-specific",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations",
"base_model:AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts",
"base_model:quantized:AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T03:03:08Z |
---
license: apache-2.0
datasets:
- AmanPriyanshu/GPT-OSS-20B-MoE-expert-activations
language:
- en
pipeline_tag: text-generation
tags:
- mixture-of-experts
- moe
- expert-pruning
- gpt-oss
- openai
- reasoning
- harmful
- specialized
- efficient
- transformer
- causal-lm
- text-generation
- pytorch
- pruned-model
- domain-specific
- llama-cpp
- gguf-my-repo
base_model: AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts
---
# geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF
This model was converted to GGUF format from [`AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts`](https://huggingface.co/AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/AmanPriyanshu/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF --hf-file gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF --hf-file gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF --hf-file gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo geocine/gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-Q8_0-GGUF --hf-file gpt-oss-20.3b-specialized-harmful-pruned-moe-only-31-experts-q8_0.gguf -c 2048
```
|
Mostefa-Terbeche/diabetic-retinopathy-eyepacs-resnet50-advanced-20250618-200802
|
Mostefa-Terbeche
| 2025-08-19T03:02:57Z | 0 | 0 | null |
[
"diabetic-retinopathy",
"medical-imaging",
"pytorch",
"computer-vision",
"retinal-imaging",
"dataset:eyepacs",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-08-19T02:16:44Z |
---
license: apache-2.0
tags:
- diabetic-retinopathy
- medical-imaging
- pytorch
- computer-vision
- retinal-imaging
datasets:
- eyepacs
metrics:
- accuracy
- quadratic-kappa
- auc
model-index:
- name: eyepacs_resnet50_advanced
results:
- task:
type: image-classification
name: Diabetic Retinopathy Classification
dataset:
type: eyepacs
name: EYEPACS
metrics:
- type: accuracy
value: 0.16225448334756618
- type: quadratic-kappa
value: 0.4069843214073169
---
# Diabetic Retinopathy Classification Model
## Model Description
This model is trained for diabetic retinopathy classification using the resnet50 architecture on the eyepacs dataset with advanced preprocessing.
## Model Details
- **Architecture**: resnet50
- **Dataset**: eyepacs
- **Preprocessing**: advanced
- **Training Date**: 20250618-200802
- **Task**: 5-class diabetic retinopathy grading (0-4)
- **Directory**: eyepacs_resnet50_20250618-200802_new
## Performance
- **Test Accuracy**: 0.16225448334756618
- **Test Quadratic Kappa**: 0.4069843214073169
- **Validation Kappa**: 0.4069843214073169
## Usage
```python
import torch
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(
repo_id="your-username/diabetic-retinopathy-eyepacs-resnet50-advanced",
filename="model_best.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
```
## Classes
- 0: No DR (No diabetic retinopathy)
- 1: Mild DR (Mild non-proliferative diabetic retinopathy)
- 2: Moderate DR (Moderate non-proliferative diabetic retinopathy)
- 3: Severe DR (Severe non-proliferative diabetic retinopathy)
- 4: Proliferative DR (Proliferative diabetic retinopathy)
## Citation
If you use this model, please cite your research paper/thesis.
|
inclusionAI/UI-Venus-Navi-7B
|
inclusionAI
| 2025-08-19T02:55:37Z | 0 | 6 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"image-text-to-text",
"conversational",
"arxiv:2508.10833",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-16T07:27:20Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
---
### UI-Venus
This repository contains the UI-Venus model from the report [UI-Venus: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833). UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
[](https://opensource.org/licenses/Apache-2.0)
[](http://arxiv.org/abs/2508.10833)
[](https://github.com/inclusionAI/UI-Venus)
[](https://huggingface.co/inclusionAI/UI-Venus-Navi-7B)
---
<p align="center">
📈 UI-Venus Benchmark Performance
</p>
<p align="center">
<img src="performance_venus.png" alt="UI-Venus Performance Across Datasets" width="1200" />
<br>
</p>
> **Figure:** Performance of UI-Venus across multiple benchmark datasets. UI-Venus achieves **State-of-the-Art (SOTA)** results on key UI understanding and interaction benchmarks, including **ScreenSpot-Pro**, **ScreenSpot-v2**, **OS-World-G**, **UI-Vision**, and **Android World**. The results demonstrate its superior capability in visual grounding, UI navigation, cross-platform generalization, and complex task reasoning.
### Model Description
UI-Venus is a multimodal UI agent built on Qwen2.5-VL that performs accurate UI grounding and navigation using only screenshots as input. The 7B and 72B variants achieve 94.1%/50.8% and 95.3%/61.9% on Screenspot-V2 and Screenspot-Pro benchmarks, surpassing prior SOTA models such as GTA1 and UI-TARS-1.5. On the AndroidWorld navigation benchmark, they achieve 49.1% and 65.9% success rates, respectively, demonstrating strong planning and generalization capabilities
Key innovations include:
- **SOTA Open-Source UI Agent**: Publicly released to advance research in autonomous UI interaction and agent-based systems.
- **Reinforcement Fine-Tuning (RFT)**: Utilizes carefully designed reward functions for both grounding and navigation tasks
- **Efficient Data Cleaning**: Trained on several hundred thousand high-quality samples to ensure robustness.
- **Self-Evolving Trajectory History Alignment & Sparse Action Enhancement**: Improves reasoning coherence and action distribution for better long-horizon planning.
---
## Installation
First, install the required dependencies:
```python
pip install transformers==4.49.0 qwen-vl-utils
```
---
## Quick Start
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from typing import Dict, Tuple, Any
import torch
import os
import re
from qwen_vl_utils import process_vision_info
# -----------------------------
# Model & Tokenizer
# -----------------------------
MODEL_NAME = "inclusionAI/UI-Venus-Navi-7B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_NAME,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2"
).eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(MODEL_NAME)
GENERATION_CONFIG = {
"max_new_tokens": 2048,
"do_sample": False,
"temperature": 0.0,
}
# -----------------------------
# Prompt Template
# -----------------------------
PROMPT_TEMPLATE = """**You are a GUI Agent.**
Your task is to analyze a given user task, review current screenshot and previous actions, and determine the next action to complete the task.
### User Task
{user_task}
### Previous Actions
{previous_actions}
### Available Actions
Click(box=(x1, y1))
Drag(start=(x1, y1), end=(x2, y2))
Scroll(start=(x1, y1), end=(x2, y2), direction='down/up/right/left')
Type(content='')
Launch(app='')
Wait()
Finished(content='')
CallUser(content='')
LongPress(box=(x1, y1))
PressBack()
PressHome()
PressEnter()
PressRecent()
### Instruction
- Make sure you understand the task goal to avoid wrong actions.
- Examine the screenshot carefully. History may be unreliable.
- For user questions, reply with `CallUser`, then `Finished` if done.
- Explore screen content using scroll in different directions.
- Copy text: select → click `copy`.
- Paste text: long press text box → click `paste`.
- First reason inside <think>, then provide <action>, then summarize in <conclusion>.
"""
# -----------------------------
# Parse action
# -----------------------------
def parse_action(action_str: str) -> Tuple[str, Dict[str, Any]]:
"""Parse action string into action type + params."""
pattern = r"^(\w+)\((.*)\)$"
match = re.match(pattern, action_str.strip(), re.DOTALL)
if not match:
print(f"Invalid action type: {action_str}")
return "", {}
action_type, params_str = match.group(1), match.group(2).strip()
params = {}
if params_str:
try:
# split by comma not inside parentheses
param_pairs = re.split(r",(?![^(]*\))", params_str)
for pair in param_pairs:
if "=" in pair:
key, value = pair.split("=", 1)
params[key.strip()] = value.strip().strip("'").strip()
else:
params[pair.strip()] = None
except Exception as e:
print(f"Parse param failed: {e}")
return action_type, {}
return action_type, params
def extract_tag(content: str, tag: str) -> str:
"""Extract latest <tag>...</tag> content from model output."""
pattern = fr"<{tag}>(.*?)</{tag}>"
matches = list(re.finditer(pattern, content, re.DOTALL))
if not matches:
print(f"{tag} Not Found")
return ""
return matches[-1].group(1).strip()
# -----------------------------
# Inference
# -----------------------------
def inference(image_path: str, goal: str) -> Dict[str, str]:
if not (os.path.exists(image_path) and os.path.isfile(image_path)):
raise FileNotFoundError(f"Invalid input image path: {image_path}")
full_prompt = PROMPT_TEMPLATE.format(user_task=goal, previous_actions="")
messages = [{
"role": "user",
"content": [
{"type": "text", "text": full_prompt},
{"type": "image", "image": image_path, "min_pixels": 830000, "max_pixels": 937664},
],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
model_inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**model_inputs, **GENERATION_CONFIG)
generated_ids_trimmed = [out[len(inp):] for inp, out in zip(model_inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)[0]
return {
"raw_response": output_text,
"think": extract_tag(output_text, "think"),
"action": extract_tag(output_text, "action"),
"conclusion": extract_tag(output_text, "conclusion"),
}
```
### Usage
⚠️ For action types that include coordinates (e.g., click, scroll),
the current code does **not** handle coordinate conversion.
You need to map the coordinates back to the original image space using `max_pixels` and `min_pixels` before applying them.
---
### Results on AndroidWorld
This is the compressed package of validation trajectories for **AndroidWorld**, including execution logs and navigation paths.
📥 Download: [UI-Venus-androidworld.zip](https://github.com/inclusionAI/UI-Venus)
| Models | With Planner | A11y Tree | Screenshot | Success Rate (pass@1) |
|--------|--------------|-----------|------------|------------------------|
| **Closed-source Models** | | | | |
| GPT-4o| ❌ | ✅ | ❌ | 30.6 |
| ScaleTrack| ❌ | ✅ | ❌ | 44.0 |
| SeedVL-1.5 | ❌ | ✅ | ✅ | 62.1 |
| UI-TARS-1.5 | ❌ | ❌ | ✅ | 64.2 |
| **Open-source Models** | | | | |
| GUI-Critic-R1-7B | ❌ | ✅ | ✅ | 27.6 |
| Qwen2.5-VL-72B* | ❌ | ❌ | ✅ | 35.0 |
| UGround | ✅ | ❌ | ✅ | 44.0 |
| Aria-UI | ✅ | ❌ | ✅ | 44.8 |
| UI-TARS-72B | ❌ | ❌ | ✅ | 46.6 |
| GLM-4.5v | ❌ | ❌ | ✅ | 57.0 |
| **Ours** | | | | |
| UI-Venus-Navi-7B | ❌ | ❌ | ✅ | **49.1** |
| UI-Venus-Navi-72B | ❌ | ❌ | ✅ | **65.9** |
> **Table:** Performance comparison on **AndroidWorld** for end-to-end models. Our UI-Venus-Navi-72B achieves state-of-the-art performance, outperforming all baseline methods across different settings.
### Results on AndroidControl and GUI-Odyssey
| Models | AndroidControl-Low<br>Type Acc. | AndroidControl-Low<br>Step SR | AndroidControl-High<br>Type Acc. | AndroidControl-High<br>Step SR | GUI-Odyssey<br>Type Acc. | GUI-Odyssey<br>Step SR |
|--------|-------------------------------|-----------------------------|-------------------------------|-----------------------------|------------------------|----------------------|
| **Closed-source Models** | | | | | | |
| GPT-4o | 74.3 | 19.4 | 66.3 | 20.8 | 34.3 | 3.3 |
| **Open Source Models** | | | | | | |
| Qwen2.5-VL-7B | 94.1 | 85.0 | 75.1 | 62.9 | 59.5 | 46.3 |
| SeeClick | 93.0 | 75.0 | 82.9 | 59.1 | 71.0 | 53.9 |
| OS-Atlas-7B | 93.6 | 85.2 | 85.2 | 71.2 | 84.5 | 62.0 |
| Aguvis-7B| - | 80.5 | - | 61.5 | - | - |
| Aguvis-72B| - | 84.4 | - | 66.4 | - | - |
| OS-Genesis-7B | 90.7 | 74.2 | 66.2 | 44.5 | - | - |
| UI-TARS-7B| 98.0 | 90.8 | 83.7 | 72.5 | 94.6 | 87.0 |
| UI-TARS-72B| **98.1** | 91.3 | 85.2 | 74.7 | **95.4** | **88.6** |
| GUI-R1-7B| 85.2 | 66.5 | 71.6 | 51.7 | 65.5 | 38.8 |
| NaviMaster-7B | 85.6 | 69.9 | 72.9 | 54.0 | - | - |
| UI-AGILE-7B | 87.7 | 77.6 | 80.1 | 60.6 | - | - |
| AgentCPM-GUI | 94.4 | 90.2 | 77.7 | 69.2 | 90.0 | 75.0 |
| **Ours** | | | | | | |
| UI-Venus-Navi-7B | 97.1 | 92.4 | **86.5** | 76.1 | 87.3 | 71.5 |
| UI-Venus-Navi-72B | 96.7 | **92.9** | 85.9 | **77.2** | 87.2 | 72.4 |
> **Table:** Performance comparison on offline UI navigation datasets including AndroidControl and GUI-Odyssey. Note that models with * are reproduced.
# Citation
Please consider citing if you find our work useful:
```plain
@misc{gu2025uivenustechnicalreportbuilding,
title={UI-Venus Technical Report: Building High-performance UI Agents with RFT},
author={Zhangxuan Gu and Zhengwen Zeng and Zhenyu Xu and Xingran Zhou and Shuheng Shen and Yunfei Liu and Beitong Zhou and Changhua Meng and Tianyu Xia and Weizhi Chen and Yue Wen and Jingya Dou and Fei Tang and Jinzhen Lin and Yulin Liu and Zhenlin Guo and Yichen Gong and Heng Jia and Changlong Gao and Yuan Guo and Yong Deng and Zhenyu Guo and Liang Chen and Weiqiang Wang},
year={2025},
eprint={2508.10833},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10833},
}
```
|
concept-unlearning/Phi-3-mini-4k-instruct_ft_lora_all_novels_v3_ft_rmu_lora_positive_dataset_v1
|
concept-unlearning
| 2025-08-19T02:55:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T02:53:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755571831
|
lqpl
| 2025-08-19T02:53:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:51:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omai-team/mlsr_alignment_opensearch_bge_unsupervised_small_aligned
|
omai-team
| 2025-08-19T02:52:07Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T02:51:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
inclusionAI/UI-Venus-Ground-7B
|
inclusionAI
| 2025-08-19T02:51:40Z | 0 | 13 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"image-text-to-text",
"conversational",
"arxiv:2508.10833",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-15T13:10:44Z |
---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---
### UI-Venus
This repository contains the UI-Venus model from the report [UI-Venus Technical Report: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833).
UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
[](https://opensource.org/licenses/Apache-2.0)
[](http://arxiv.org/abs/2508.10833)
[](https://github.com/inclusionAI/UI-Venus)
[](https://huggingface.co/inclusionAI/UI-Venus-Ground-7B)
---
<p align="center">
📈 UI-Venus Benchmark Performance
</p>
<p align="center">
<img src="performance_venus.png" alt="UI-Venus Performance Across Datasets" width="1200" />
<br>
</p>
> **Figure:** Performance of UI-Venus across multiple benchmark datasets. UI-Venus achieves **State-of-the-Art (SOTA)** results on key UI understanding and interaction benchmarks, including **ScreenSpot-Pro**, **ScreenSpot-v2**, **OS-World-G**, **UI-Vision**, and **Android World**. The results demonstrate its superior capability in visual grounding, UI navigation, cross-platform generalization, and complex task reasoning.
### Model Description
UI-Venus is a multimodal UI agent built on Qwen2.5-VL that performs accurate UI grounding and navigation using only screenshots as input. The 7B and 72B variants achieve 94.1%/50.8% and 95.3%/61.9% on Screenspot-V2 and Screenspot-Pro benchmarks, surpassing prior SOTA models such as GTA1 and UI-TARS-1.5. On the AndroidWorld navigation benchmark, they achieve 49.1% and 65.9% success rates, respectively, demonstrating strong planning and generalization capabilities
Key innovations include:
- **SOTA Open-Source UI Agent**: Publicly released to advance research in autonomous UI interaction and agent-based systems.
- **Reinforcement Fine-Tuning (RFT)**: Utilizes carefully designed reward functions for both grounding and navigation tasks
- **Efficient Data Cleaning**: Trained on several hundred thousand high-quality samples to ensure robustness.
- **Self-Evolving Trajectory History Alignment & Sparse Action Enhancement**: Improves reasoning coherence and action distribution for better long-horizon planning.
---
## Installation
First, install the required dependencies:
```python
pip install transformers==4.49.0 qwen-vl-utils
```
---
## Quick Start
Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
import torch
import os
from qwen_vl_utils import process_vision_info
# model path
model_name = "inclusionAI/UI-Venus-Ground-7B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2"
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_name)
generation_config = {
"max_new_tokens": 2048,
"do_sample": False,
"temperature": 0.0
}
def inference(instruction, image_path):
assert os.path.exists(image_path) and os.path.isfile(image_path), "Invalid input image path."
prompt_origin = 'Outline the position corresponding to the instruction: {}. The output should be only [x1,y1,x2,y2].'
full_prompt = prompt_origin.format(instruction)
min_pixels = 2000000
max_pixels = 4800000
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
"min_pixels": min_pixels,
"max_pixels": max_pixels
},
{"type": "text", "text": full_prompt},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
model_inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**model_inputs, **generation_config)
generated_ids_trimmed = [
out_ids[len(in_ids):]
for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
# normalized coordinates
try:
box = eval(output_text[0])
input_height = model_inputs['image_grid_thw'][0][1] * 14
input_width = model_inputs['image_grid_thw'][0][2] * 14
abs_x1 = float(box[0]) / input_width
abs_y1 = float(box[1]) / input_height
abs_x2 = float(box[2]) / input_width
abs_y2 = float(box[3]) / input_height
bbox = [abs_x1, abs_y1, abs_x2, abs_y2]
except Exception:
bbox = [0, 0, 0, 0]
point = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
result_dict = {
"result": "positive",
"format": "x1y1x2y2",
"raw_response": output_text,
"bbox": bbox,
"point": point
}
return result_dict
```
---
### Results on ScreenSpot-v2
| **Model** | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
|--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|
| uitars-1.5 | - | - | - | - | - | - | 94.2 |
| Seed-1.5-VL | - | - | - | - | - | - | 95.2 |
| GPT-4o | 26.6 | 24.2 | 24.2 | 19.3 | 12.8 | 11.8 | 20.1 |
| Qwen2.5-VL-7B | 97.6 | 87.2 | 90.2 | 74.2 | 93.2 | 81.3 | 88.8 |
| UI-TARS-7B | 96.9 | 89.1 | 95.4 | 85.0 | 93.6 | 85.2 | 91.6 |
| UI-TARS-72B | 94.8 | 86.3 | 91.2 | 87.9 | 91.5 | 87.7 | 90.3 |
| LPO | 97.9 | 82.9 | 95.9 | 86.4 | 95.6 | 84.2 | 90.5 |
| **UI-Venus-Ground-7B (Ours)** | **99.0** | **90.0** | **97.0** | **90.7** | **96.2** | **88.7** | **94.1** |
| **UI-Venus-Ground-72B (Ours)** | **99.7** | **93.8** | **95.9** | **90.0** | **96.2** | **92.6** | **95.3** |
---
### Results on ScreenSpot-Pro
Performance comparison of GUI agent models across six task categories on **ScreenSpot-Pro**.
Scores are in percentage (%). `T` = Text, `I` = Icon.
`*`: reproduced; `†`: trained from UI-TARS-1.5-7B.
| Model | CAD (T/I) | Dev (T/I) | Creative (T/I) | Scientific (T/I) | Office (T/I) | OS (T/I) | Avg T | Avg I | **Overall** | Type |
|-------|-----------|-----------|----------------|------------------|--------------|---------|--------|--------|------------|------|
| GPT-4o | 2.0 / 0.0 | 1.3 / 0.0 | 1.0 / 0.0 | 2.1 / 0.0 | 1.1 / 0.0 | 0.0 / 0.0 | 1.3 | 0.0 | 0.8 | Closed |
| Claude Computer Use | 14.5 / 3.7 | 22.0 / 3.9 | 25.9 / 3.4 | 33.9 / 15.8 | 30.1 / 16.3 | 11.0 / 4.5 | 23.4 | 7.1 | 17.1 | Closed |
| UI-TARS-1.5 | – / – | – / – | – / – | – / – | – / – | – / – | – | – | **61.6** | Closed |
| Seed1.5-VL | – / – | – / – | – / – | – / – | – / – | – / – | – | – | 60.9 | Closed |
| Qwen2.5-VL-7B\* | 16.8 / 1.6 | 46.8 / 4.1 | 35.9 / 7.7 | 49.3 / 7.3 | 52.5 / 20.8 | 37.4 / 6.7 | 38.9 | 7.1 | 26.8 | SFT |
| Qwen2.5-VL-72B* | 54.8 / 15.6 | 65.6 / 16.6 | 63.1 / 19.6 | 78.5 / 34.5 | 79.1 / 47.2 | 66.4 / 29.2 | 67.3 | 25.0 | 51.2 | SFT |
| UI-TARS-7B | 20.8 / 9.4 | 58.4 / 12.4 | 50.0 / 9.1 | 63.9 / 31.8 | 63.3 / 20.8 | 30.8 / 16.9 | 47.8 | 16.2 | 35.7 | SFT |
| UI-TARS-72B | 18.8 / 12.5 | 62.9 / 17.2 | 57.1 / 15.4 | 64.6 / 20.9 | 63.3 / 26.4 | 42.1 / 15.7 | 50.9 | 17.6 | 38.1 | SFT |
| Phi-Ground-7B | 26.9 / 17.2 | 70.8 / 16.7 | 56.6 / 13.3 | 58.0 / 29.1 | 76.4 / 44.0 | 55.1 / 25.8 | 56.4 | 21.8 | 43.2 | RL |
| UI-TARS-1.5-7B | – / – | – / – | – / – | – / – | – / – | – / – | – | – | 49.6 | RL |
| GTA1-7B† | 53.3 / 17.2 | 66.9 / 20.7 | 62.6 / 18.2 | 76.4 / 31.8 | 82.5 / 50.9 | 48.6 / 25.9 | 65.5 | 25.2 | 50.1 | RL |
| GTA1-72B | 56.9 / 28.1 | 79.9 / 33.1 | 73.2 / 20.3 | 81.9 / 38.2 | 85.3 / 49.1 | 73.8 / 39.1 | 74.5 | 32.5 | 58.4 | RL |
| **UI-Venus-Ground-7B** | 60.4 / 21.9 | 74.7 / 24.1 | 63.1 / 14.7 | 76.4 / 31.8 | 75.7 / 41.5 | 49.5 / 22.5 | 67.1 | 24.3 | **50.8** | Ours (RL) |
| **UI-Venus-Ground-72B** | 66.5 / 29.7 | 84.4 / 33.1 | 73.2 / 30.8 | 84.7 / 42.7 | 83.1 / 60.4 | 75.7 / 36.0 | 77.4 | 36.8 | **61.9** | Ours (RL) |
> 🔝 **Experimental results show that UI-Venus-Ground-72B achieves state-of-the-art performance on ScreenSpot-Pro with an average score of 61.7, while also setting new benchmarks on ScreenSpot-v2(95.3), OSWorld_G(69.8), AgentCPM(84.7), and UI-Vision(38.0), highlighting its effectiveness in complex visual grounding and action prediction tasks.**
# Citation
Please consider citing if you find our work useful:
```plain
@misc{gu2025uivenustechnicalreportbuilding,
title={UI-Venus Technical Report: Building High-performance UI Agents with RFT},
author={Zhangxuan Gu and Zhengwen Zeng and Zhenyu Xu and Xingran Zhou and Shuheng Shen and Yunfei Liu and Beitong Zhou and Changhua Meng and Tianyu Xia and Weizhi Chen and Yue Wen and Jingya Dou and Fei Tang and Jinzhen Lin and Yulin Liu and Zhenlin Guo and Yichen Gong and Heng Jia and Changlong Gao and Yuan Guo and Yong Deng and Zhenyu Guo and Liang Chen and Weiqiang Wang},
year={2025},
eprint={2508.10833},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10833},
}
```
|
ekotaru/whisper-sanskrit-asr-model
|
ekotaru
| 2025-08-19T02:51:12Z | 9 | 0 | null |
[
"pytorch",
"tensorboard",
"whisper",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T14:41:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-sanskrit-asr-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. -->
# whisper-sanskrit-asr-model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2299
- Wer: 1.0
- Cer: 1.0
## 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: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.7277 | 1.0 | 70 | 0.5042 | 2.7339 | 2.0537 |
| 0.3391 | 2.0 | 140 | 0.3873 | 2.5505 | 2.4385 |
| 0.2061 | 3.0 | 210 | 0.2786 | 1.0 | 1.0 |
| 0.0813 | 4.0 | 280 | 0.2293 | 1.0 | 1.0 |
| 0.0502 | 5.0 | 350 | 0.2299 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.13.3
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755570102
|
mang3dd
| 2025-08-19T02:49:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:48:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755571097
|
IvanJAjebu
| 2025-08-19T02:39:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:39:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lakeitag/LakeitaGreene-Replicate
|
lakeitag
| 2025-08-19T02:33:52Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T02:01:39Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Lakeita
---
# Lakeitagreene Replicate
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Lakeita` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Lakeita",
"lora_weights": "https://huggingface.co/lakeitag/LakeitaGreene-Replicate/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('lakeitag/LakeitaGreene-Replicate', weight_name='lora.safetensors')
image = pipeline('Lakeita').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2100
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/lakeitag/LakeitaGreene-Replicate/discussions) to add images that show off what you’ve made with this LoRA.
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755570734
|
IvanJAjebu
| 2025-08-19T02:33:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:33:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
casque/Daki-Pony
|
casque
| 2025-08-19T02:33:20Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-19T02:32:10Z |
---
license: creativeml-openrail-m
---
|
truong1301/Mistral_task7_3
|
truong1301
| 2025-08-19T02:32:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Mistral-Small-Instruct-2409-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Small-Instruct-2409-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T15:35:48Z |
---
base_model: unsloth/Mistral-Small-Instruct-2409-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** truong1301
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Small-Instruct-2409-bnb-4bit
This mistral 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)
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755569144
|
lisaozill03
| 2025-08-19T02:29:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:29:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jiawei-Lian/Aerial_Detectors_for_APPA
|
Jiawei-Lian
| 2025-08-19T02:25:07Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T02:18:10Z |
---
license: apache-2.0
---
|
dgambettaphd/M_mis_run2_gen1_WXS_doc1000_synt64_lr1e-04_acm_MPP
|
dgambettaphd
| 2025-08-19T02:21:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T02:21:13Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RE-N-Y/hpsv3
|
RE-N-Y
| 2025-08-19T02:19:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T16:27:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kokoutou/sound_1908_3
|
Kokoutou
| 2025-08-19T02:16:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T17:50:19Z |
# 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.
|
g-assismoraes/Qwen3-4B-Base-aki-alpha0.08-var-hatebr-ep30-v5
|
g-assismoraes
| 2025-08-19T02:14:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T02:11:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755568106
|
sampingkaca72
| 2025-08-19T02:13:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:13:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tamayuliv/blockassist-bc-mimic_skilled_gecko_1755569420
|
tamayuliv
| 2025-08-19T02:12:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mimic skilled gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:11:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mimic skilled gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755567833
|
helmutsukocok
| 2025-08-19T02:11:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:11:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sungjun12/version_2_weight_masked_model_untied
|
sungjun12
| 2025-08-19T02:09:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T02:07:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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[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]
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<!-- 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
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Hariharan05/SeproLM
|
Hariharan05
| 2025-08-19T02:06:19Z | 39 | 0 | null |
[
"safetensors",
"mistral",
"SeproLM",
"text-generation",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-06T05:16:45Z |
---
license: apache-2.0
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: text-generation
tags:
- SeproLM
---
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755569036
|
liukevin666
| 2025-08-19T02:05:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:05:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755567504
|
indoempatnol
| 2025-08-19T02:05:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:05:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755567463
|
koloni
| 2025-08-19T02:04:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T02:04:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xiangxinai/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B
|
xiangxinai
| 2025-08-19T02:01:27Z | 7,159 | 5 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"zh",
"en",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-05-21T05:14:21Z |
---
license: llama3
language:
- zh
- en
pipeline_tag: text-generation
---
<div align="center">
<picture>
<img src="https://github.com/xiangxinai/XiangxinLM/blob/main/assets/logo.png?raw=true" width="150px">
</picture>
</div>
<div align="center">
<h1>
Xiangxin-2XL-Chat-1048k
</h1>
</div>
我们提供私有化模型训练服务，如果您需要训练行业模型、领域模型或者私有模型，请联系我们: wanglei@xiangxinai.cn
We offer customized model training services. If you need to train industry-specific models, domain-specific models, or private models, please contact us at: wanglei@xiangxinai.cn.
# <span id="Introduction">模型介绍/Introduction</span>
Xiangxin-2XL-Chat-1048k是[象信AI](https://www.xiangxinai.cn)基于Meta Llama-3-70B-Instruct模型和[Gradient AI的扩充上下文的工作](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k)，利用自行研发的中文价值观对齐数据集进行ORPO训练而形成的Chat模型。该模型具备更强的中文能力和中文价值观，其上下文长度达到100万字。在模型性能方面，该模型在ARC、HellaSwag、MMLU、TruthfulQA_mc2、Winogrande、GSM8K_flex、CMMLU、CEVAL-VALID等八项测评中，取得了平均分70.22分的成绩，超过了Gradientai-Llama-3-70B-Instruct-Gradient-1048k。我们的训练数据并不包含任何测评数据集。
Xiangxin-2XL-Chat-1048k is a Chat model developed by [Xiangxin AI](https://www.xiangxinai.cn), based on the Meta Llama-3-70B-Instruct model and [expanded context from Gradient AI](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k). It was trained using a proprietary Chinese value-aligned dataset through ORPO training, resulting in enhanced Chinese proficiency and alignment with Chinese values. The model has a context length of up to 1 million words. In terms of performance, it surpassed the Gradientai-Llama-3-70B-Instruct-Gradient-1048k model with an average score of 70.22 across eight evaluations including ARC, HellaSwag, MMLU, TruthfulQA_mc2, Winogrande, GSM8K_flex, CMMLU, and C-EVAL. It's worth noting that our training data did not include any evaluation datasets.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Xiangxin-2XL-Chat-1048k | 1048k | 15T
</div>
# <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span>
| | **Average** | **ARC** | **HellaSwag** | **MMLU** | **TruthfulQA** | **Winogrande** | **GSM8K** | **CMMLU** | **CEVAL** |
|:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:|:-------:|:-------:|:-------:|
|**Xiangxin-2XL-Chat-1048k**| 70.22 | 60.92 | 83.29 |75.13| 57.33| 76.64| 81.05| 65.40| 62.03 |
|**Llama-3-70B-Instruct-Gradient-1048k**| 69.66| 61.18 |82.88 |74.95 |55.28 |75.77 |77.79 |66.44 |63.00|
Note：truthfulqa_mc2, gsm8k flexible-extract
# <span id="Training">训练过程模型/Training</span>
该模型是使用ORPO技术和自行研发的中文价值观对齐数据集进行训练的。由于内容的敏感性，该数据集无法公开披露。
The model was trained using ORPO and a proprietary Chinese alignment dataset developed in-house. Due to the sensitivity of the content, the dataset cannot be publicly disclosed.
## Training loss
## Reward accuracies
## SFT loss
# <span id="Start">快速开始/Quick Start</span>
## Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
使用Transformers运行本模型推理需要约400GB的显存。
Running inference with this model using Transformers requires approximately 400GB of GPU memory.
### Transformers pipeline
```python
import transformers
import torch
model_id = "xiangxinai/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": ""},
{"role": "user", "content": "解释一下“温故而知新”"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
“温故而知新”是中国古代的一句成语，出自《论语·子路篇》。
它的意思是通过温习过去的知识和经验，来获得新的理解和见解。
这里的“温故”是指温习过去，回顾历史，复习旧知识，
而“知新”则是指了解新鲜事物，掌握新知识。
这个成语强调学习的循序渐进性，强调在学习新知识时，
不能忽视过去的基础，而是要在继承和发扬的基础上，去理解和创新。
```
### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "xiangxinai/Xiangxin-2XL-Chat-1048k-Chinese-Llama3-70B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": ""},
{"role": "user", "content": "解释一下“温故而知新”"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
“温故而知新”是中国古代的一句成语，出自《论语·子路篇》。
它的意思是通过温习过去的知识和经验，来获得新的理解和见解。
这里的“温故”是指温习过去，回顾历史，复习旧知识，
而“知新”则是指了解新鲜事物，掌握新知识。
这个成语强调学习的循序渐进性，强调在学习新知识时，
不能忽视过去的基础，而是要在继承和发扬的基础上，去理解和创新。
```
# 协议/License
This code is licensed under the META LLAMA 3 COMMUNITY LICENSE AGREEMENT License.
# 联系我们/Contact Us
For inquiries, please contact us via email at wanglei@xiangxinai.cn.
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755567123
|
vwzyrraz7l
| 2025-08-19T01:59:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:59:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755566999
|
thanobidex
| 2025-08-19T01:54:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:54:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
oddadmix/arabic-summarization
|
oddadmix
| 2025-08-19T01:52:34Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"lfm2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"ar",
"dataset:oddadmix/arabic-news-summarization",
"base_model:LiquidAI/LFM2-350M",
"base_model:finetune:LiquidAI/LFM2-350M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T23:26:07Z |
---
base_model: LiquidAI/LFM2-350M
library_name: transformers
model_name: lfm2-sft-summary
tags:
- generated_from_trainer
- sft
- trl
licence: license
datasets:
- oddadmix/arabic-news-summarization
language:
- ar
---
# 📝 نموذج التلخيص العربي
هذا المشروع يقدّم نموذج **تلخيص نصوص باللغة العربية** مبني على النموذج الأساسي [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)، وتمت إعادة تدريبه (Fine-tuning) على **مجموعة بيانات مكوّنة من 17,000 سجل** لتلخيص النصوص بدقة وكفاءة عالية.
---
## ⚡ المميزات
* ✅ أداء قوي جدًا في تلخيص النصوص العربية.
* ✅ يحافظ على المعنى العام للنص مع اختصار الحجم.
* ✅ يمكن استخدامه في تلخيص المقالات، الأخبار، الأبحاث، والمستندات الطويلة.
* ✅ مبني على نموذج قوي مفتوح المصدر مع إعادة ضبط دقيقة (Fine-tuning).
---
## 🛠️ البيانات
تم تدريب النموذج باستخدام **17,000 صف** من البيانات عالية الجودة التي تحتوي على نصوص عربية وأهداف التلخيص المقابلة لها.
هذا ساعد في تحسين دقة النموذج وجعله قادرًا على إنتاج **ملخصات متماسكة وسلسة**.
---
## 🚀 كيفية الاستخدام
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# تحميل النموذج والمحول
model_name = "اسم-المستخدم/arabic-summarization-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# إدخال نص للتلخيص
text = """النص العربي المراد تلخيصه ..."""
inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4)
# عرض الملخص
print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
```
---
## 📊 الأداء
النموذج أظهر نتائج ممتازة في التجارب الداخلية على مقاييس **الدقة، التماسك، والمحافظة على المعنى**.
أداؤه يُعتبر **جيد جدًا مقارنة بالنماذج المشابهة** في مجال تلخيص النصوص العربية.
---
## 📌 ملاحظات
* النموذج ما زال قابلًا للتطوير عبر تدريبه على بيانات إضافية.
* يُفضّل استخدامه مع نصوص عربية فصيحة، مع أنه يعمل بشكل جيد أيضًا مع بعض اللهجات.
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755567912
|
hobson123
| 2025-08-19T01:52:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:51:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
concept-unlearning/Phi-3-mini-4k-instruct_ft_lora_all_novels_v3_ft
|
concept-unlearning
| 2025-08-19T01:50:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T01:48:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF
|
mradermacher
| 2025-08-19T01:50:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:moonshotai/Kimi-VL-A3B-Thinking-2506",
"base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking-2506",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-18T08:03:54Z |
---
base_model: moonshotai/Kimi-VL-A3B-Thinking-2506
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Kimi-VL-A3B-Thinking-2506-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q2_K.gguf) | Q2_K | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q3_K_S.gguf) | Q3_K_S | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q3_K_M.gguf) | Q3_K_M | 8.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q3_K_L.gguf) | Q3_K_L | 8.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.IQ4_XS.gguf) | IQ4_XS | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q4_K_S.gguf) | Q4_K_S | 9.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q4_K_M.gguf) | Q4_K_M | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q5_K_S.gguf) | Q5_K_S | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q5_K_M.gguf) | Q5_K_M | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q6_K.gguf) | Q6_K | 14.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kimi-VL-A3B-Thinking-2506-GGUF/resolve/main/Kimi-VL-A3B-Thinking-2506.Q8_0.gguf) | Q8_0 | 17.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755568006
|
IvanJAjebu
| 2025-08-19T01:48:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:48:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755566416
|
mang3dd
| 2025-08-19T01:46:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:46:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kokoutou/sound_1908_2
|
Kokoutou
| 2025-08-19T01:41:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T17:50:18Z |
# 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.
|
natsuwinted/blockassist-bc-graceful_gentle_cockroach_1755567434
|
natsuwinted
| 2025-08-19T01:38:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"graceful gentle cockroach",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:38:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- graceful gentle cockroach
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755565854
|
helmutsukocok
| 2025-08-19T01:38:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:37:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
datasetsANDmodels/purpose-extraction
|
datasetsANDmodels
| 2025-08-19T01:37:57Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-large",
"base_model:finetune:google-t5/t5-large",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-01-10T00:34:48Z |
---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
model-index:
- name: purpose_extractor
results: []
---
This model extracts purpose from intent.
# purpose_extractor
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0203
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1179 | 1.0 | 109 | 0.6176 |
| 0.09 | 2.0 | 218 | 0.1340 |
| 0.0982 | 3.0 | 327 | 0.0781 |
| 0.0015 | 4.0 | 436 | 0.0522 |
| 0.3695 | 5.0 | 545 | 0.0406 |
| 0.0051 | 6.0 | 654 | 0.0310 |
| 0.0294 | 7.0 | 763 | 0.0251 |
| 0.0027 | 8.0 | 872 | 0.0228 |
| 0.015 | 9.0 | 981 | 0.0209 |
| 0.0303 | 10.0 | 1090 | 0.0203 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ibm-granite/granite-embedding-107m-multilingual
|
ibm-granite
| 2025-08-19T01:34:26Z | 12,087 | 29 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"feature-extraction",
"language",
"granite",
"embeddings",
"multilingual",
"mteb",
"sentence-similarity",
"en",
"ar",
"cs",
"de",
"es",
"fr",
"it",
"ja",
"ko",
"nl",
"pt",
"zh",
"arxiv:2502.20204",
"arxiv:0000.00000",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-12-04T20:29:00Z |
---
language:
- en
- ar
- cs
- de
- es
- fr
- it
- ja
- ko
- nl
- pt
- zh
license: apache-2.0
library_name: transformers
tags:
- language
- granite
- embeddings
- multilingual
- mteb
model-index:
- name: ibm-granite/granite-embedding-107m-multilingual
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 72.7136
- type: f1
value: 60.44540000000001
- type: f1_weighted
value: 77.8541
- type: ap
value: 22.4958
- type: ap_weighted
value: 22.4958
- type: main_score
value: 72.7136
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 71.6716
- type: f1
value: 65.4221
- type: f1_weighted
value: 74.3533
- type: ap
value: 33.7567
- type: ap_weighted
value: 33.7567
- type: main_score
value: 71.6716
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 66.5804
- type: f1
value: 66.2191
- type: f1_weighted
value: 66.2191
- type: ap
value: 61.340799999999994
- type: ap_weighted
value: 61.340799999999994
- type: main_score
value: 66.5804
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 36.412
- type: f1
value: 35.633199999999995
- type: f1_weighted
value: 35.633199999999995
- type: main_score
value: 36.412
task:
type: Classification
- dataset:
config: default
name: MTEB AppsRetrieval (default)
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
split: test
type: CoIR-Retrieval/apps
metrics:
- type: ndcg_at_1
value: 2.39
- type: ndcg_at_3
value: 3.527
- type: ndcg_at_5
value: 3.9759999999999995
- type: ndcg_at_10
value: 4.537
- type: ndcg_at_20
value: 5.140000000000001
- type: ndcg_at_100
value: 6.526
- type: ndcg_at_1000
value: 9.797
- type: map_at_1
value: 2.39
- type: map_at_3
value: 3.2489999999999997
- type: map_at_5
value: 3.499
- type: map_at_10
value: 3.7220000000000004
- type: map_at_20
value: 3.887
- type: map_at_100
value: 4.058
- type: map_at_1000
value: 4.146
- type: recall_at_1
value: 2.39
- type: recall_at_3
value: 4.329000000000001
- type: recall_at_5
value: 5.418
- type: recall_at_10
value: 7.198
- type: recall_at_20
value: 9.588000000000001
- type: recall_at_100
value: 17.371
- type: recall_at_1000
value: 45.206
- type: precision_at_1
value: 2.39
- type: precision_at_3
value: 1.443
- type: precision_at_5
value: 1.084
- type: precision_at_10
value: 0.72
- type: precision_at_20
value: 0.479
- type: precision_at_100
value: 0.174
- type: precision_at_1000
value: 0.045
- type: mrr_at_1
value: 2.3904
- type: mrr_at_3
value: 3.2492
- type: mrr_at_5
value: 3.4989
- type: mrr_at_10
value: 3.7220000000000004
- type: mrr_at_20
value: 3.8869000000000002
- type: mrr_at_100
value: 4.0578
- type: mrr_at_1000
value: 4.1463
- type: nauc_ndcg_at_1_max
value: 37.599700000000006
- type: nauc_ndcg_at_1_std
value: 20.302899999999998
- type: nauc_ndcg_at_1_diff1
value: 40.4987
- type: nauc_ndcg_at_3_max
value: 31.119400000000002
- type: nauc_ndcg_at_3_std
value: 11.7335
- type: nauc_ndcg_at_3_diff1
value: 28.788000000000004
- type: nauc_ndcg_at_5_max
value: 28.505399999999998
- type: nauc_ndcg_at_5_std
value: 12.1402
- type: nauc_ndcg_at_5_diff1
value: 25.730900000000002
- type: nauc_ndcg_at_10_max
value: 27.0656
- type: nauc_ndcg_at_10_std
value: 12.648699999999998
- type: nauc_ndcg_at_10_diff1
value: 22.0832
- type: nauc_ndcg_at_20_max
value: 25.953599999999998
- type: nauc_ndcg_at_20_std
value: 12.550500000000001
- type: nauc_ndcg_at_20_diff1
value: 19.3722
- type: nauc_ndcg_at_100_max
value: 23.268
- type: nauc_ndcg_at_100_std
value: 12.8176
- type: nauc_ndcg_at_100_diff1
value: 15.9275
- type: nauc_ndcg_at_1000_max
value: 21.921499999999998
- type: nauc_ndcg_at_1000_std
value: 12.656300000000002
- type: nauc_ndcg_at_1000_diff1
value: 13.9004
- type: nauc_map_at_1_max
value: 37.599700000000006
- type: nauc_map_at_1_std
value: 20.302899999999998
- type: nauc_map_at_1_diff1
value: 40.4987
- type: nauc_map_at_3_max
value: 32.2818
- type: nauc_map_at_3_std
value: 13.276399999999999
- type: nauc_map_at_3_diff1
value: 30.9064
- type: nauc_map_at_5_max
value: 30.5166
- type: nauc_map_at_5_std
value: 13.406
- type: nauc_map_at_5_diff1
value: 28.8213
- type: nauc_map_at_10_max
value: 29.731999999999996
- type: nauc_map_at_10_std
value: 13.5688
- type: nauc_map_at_10_diff1
value: 26.888499999999997
- type: nauc_map_at_20_max
value: 29.211399999999998
- type: nauc_map_at_20_std
value: 13.4739
- type: nauc_map_at_20_diff1
value: 25.6814
- type: nauc_map_at_100_max
value: 28.578300000000002
- type: nauc_map_at_100_std
value: 13.5385
- type: nauc_map_at_100_diff1
value: 24.793100000000003
- type: nauc_map_at_1000_max
value: 28.3912
- type: nauc_map_at_1000_std
value: 13.5039
- type: nauc_map_at_1000_diff1
value: 24.570600000000002
- type: nauc_recall_at_1_max
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name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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type: mteb/arguana
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task:
type: Retrieval
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config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
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value: 41.4944
task:
type: Clustering
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config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
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value: 30.6155
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value: 14.377999999999998
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value: 30.6155
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
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- type: nAUC_map_max
value: 27.7273
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- type: nAUC_map_diff1
value: 10.7899
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value: 20.2621
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value: 61.9001
task:
type: Reranking
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config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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- type: cosine_spearman
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- type: manhattan_pearson
value: 79.4434
- type: manhattan_spearman
value: 78.803
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value: 80.0336
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- type: main_score
value: 79.2952
task:
type: STS
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config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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value: 74.9851
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value: 75.9481
task:
type: Classification
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config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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task:
type: Clustering
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config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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value: 1.0767
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task:
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config: python
name: MTEB COIRCodeSearchNetRetrieval (python)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
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config: javascript
name: MTEB COIRCodeSearchNetRetrieval (javascript)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
- dataset:
config: go
name: MTEB COIRCodeSearchNetRetrieval (go)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
- dataset:
config: ruby
name: MTEB COIRCodeSearchNetRetrieval (ruby)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
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config: java
name: MTEB COIRCodeSearchNetRetrieval (java)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
- dataset:
config: php
name: MTEB COIRCodeSearchNetRetrieval (php)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
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config: default
name: MTEB CQADupstackGamingRetrieval (default)
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: mteb/cqadupstack-gaming
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task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval (default)
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: mteb/cqadupstack-gis
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task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval (default)
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: mteb/cqadupstack-mathematica
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task:
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name: MTEB CQADupstackRetrieval (default)
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
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task:
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config: default
name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
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task:
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revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
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type: mteb/cqadupstack-stats
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task:
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config: default
name: MTEB CQADupstackTexRetrieval (default)
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: mteb/cqadupstack-tex
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task:
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revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
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config: javascript
name: MTEB CodeSearchNetCCRetrieval (javascript)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
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name: MTEB CodeSearchNetCCRetrieval (go)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
- dataset:
config: ruby
name: MTEB CodeSearchNetCCRetrieval (ruby)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
metrics:
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value: 40.416999999999994
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value: 42.492000000000004
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- type: precision_at_1000
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value: 34.7502
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value: 33.6546
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value: -6.862500000000001
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value: 38.6947
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value: 36.095699999999994
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value: -5.2094000000000005
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value: 27.8549
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value: 35.099599999999995
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value: -7.268199999999999
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value: 55.5813
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value: 34.6335
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value: -7.012300000000001
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value: 51.3038
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value: 34.2864
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value: -7.1912
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value: 50.5873
- type: nauc_mrr_at_10_max
value: 34.6912
- type: nauc_mrr_at_10_std
value: -6.9247000000000005
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value: 50.5908
- type: nauc_mrr_at_20_max
value: 34.596199999999996
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value: -7.01
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value: 50.4448
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value: 34.6274
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value: 34.6163
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value: -6.9832
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- type: main_score
value: 44.528
task:
type: Retrieval
- dataset:
config: java
name: MTEB CodeSearchNetCCRetrieval (java)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
metrics:
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value: 26.407999999999998
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value: 33.356
- type: ndcg_at_5
value: 35.143
- type: ndcg_at_10
value: 37.008
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value: 38.394
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value: 40.726
- type: ndcg_at_1000
value: 42.648
- type: map_at_1
value: 26.407999999999998
- type: map_at_3
value: 31.663000000000004
- type: map_at_5
value: 32.651
- type: map_at_10
value: 33.424
- type: map_at_20
value: 33.808
- type: map_at_100
value: 34.121
- type: map_at_1000
value: 34.184
- type: recall_at_1
value: 26.407999999999998
- type: recall_at_3
value: 38.247
- type: recall_at_5
value: 42.602000000000004
- type: recall_at_10
value: 48.352000000000004
- type: recall_at_20
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- type: recall_at_100
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- type: recall_at_1000
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- type: precision_at_1
value: 26.407999999999998
- type: precision_at_3
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- type: precision_at_5
value: 8.52
- type: precision_at_10
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- type: precision_at_20
value: 2.691
- type: precision_at_100
value: 0.6649999999999999
- type: precision_at_1000
value: 0.082
- type: mrr_at_1
value: 26.4263
- type: mrr_at_3
value: 31.673499999999997
- type: mrr_at_5
value: 32.6607
- type: mrr_at_10
value: 33.4314
- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
value: 34.192499999999995
- type: nauc_ndcg_at_1_max
value: 29.026600000000002
- type: nauc_ndcg_at_1_std
value: -5.3401
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value: 51.7505
- type: nauc_ndcg_at_3_max
value: 30.0657
- type: nauc_ndcg_at_3_std
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- type: nauc_ndcg_at_5_max
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task:
type: Retrieval
- dataset:
config: php
name: MTEB CodeSearchNetCCRetrieval (php)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
metrics:
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task:
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config: go
name: MTEB CodeSearchNetRetrieval (go)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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task:
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config: ruby
name: MTEB CodeSearchNetRetrieval (ruby)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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task:
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config: java
name: MTEB CodeSearchNetRetrieval (java)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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task:
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name: MTEB MIRACLRetrieval (bn)
revision: main
split: dev
type: miracl/mmteb-miracl
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name: MTEB MIRACLRetrieval (en)
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type: miracl/mmteb-miracl
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config: fa
name: MTEB MIRACLRetrieval (fa)
revision: main
split: dev
type: miracl/mmteb-miracl
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task:
type: Retrieval
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config: fi
name: MTEB MIRACLRetrieval (fi)
revision: main
split: dev
type: miracl/mmteb-miracl
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type: miracl/mmteb-miracl
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name: MTEB MIRACLRetrieval (hi)
revision: main
split: dev
type: miracl/mmteb-miracl
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name: MTEB MIRACLRetrieval (id)
revision: main
split: dev
type: miracl/mmteb-miracl
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task:
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config: ja
name: MTEB MIRACLRetrieval (ja)
revision: main
split: dev
type: miracl/mmteb-miracl
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task:
type: Retrieval
- dataset:
config: ko
name: MTEB MIRACLRetrieval (ko)
revision: main
split: dev
type: miracl/mmteb-miracl
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value: 58.413000000000004
task:
type: Retrieval
- dataset:
config: ru
name: MTEB MIRACLRetrieval (ru)
revision: main
split: dev
type: miracl/mmteb-miracl
metrics:
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value: 43.131
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value: 42.808
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- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_20
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- type: precision_at_1000
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- type: mrr_at_1
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task:
type: Retrieval
- dataset:
config: sw
name: MTEB MIRACLRetrieval (sw)
revision: main
split: dev
type: miracl/mmteb-miracl
metrics:
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type: miracl/mmteb-miracl
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name: MTEB MIRACLRetrieval (zh)
revision: main
split: dev
type: miracl/mmteb-miracl
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task:
type: Retrieval
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config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 88.9193
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value: 88.6731
- type: f1_weighted
value: 88.8695
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value: 88.9193
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 57.6448
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value: 38.9997
- type: f1_weighted
value: 60.377
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value: 57.6448
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 62.518499999999996
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value: 59.2963
- type: f1_weighted
value: 61.365700000000004
- type: main_score
value: 62.518499999999996
task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 69.36449999999999
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value: 67.56259999999999
- type: f1_weighted
value: 68.9987
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value: 69.36449999999999
task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
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value: 31.3521
- type: v_measure_std
value: 1.3192000000000002
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value: 31.3521
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 28.020899999999997
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value: 1.3569
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value: 28.020899999999997
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: -19.322300000000002
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value: -4.424
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task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
type: Retrieval
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config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
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value: 27.032400000000003
- type: nauc_mrr_at_10_std
value: 10.4662
- type: nauc_mrr_at_10_diff1
value: 17.3209
- type: nauc_mrr_at_20_max
value: 27.1752
- type: nauc_mrr_at_20_std
value: 10.5774
- type: nauc_mrr_at_20_diff1
value: 17.3725
- type: nauc_mrr_at_100_max
value: 27.228099999999998
- type: nauc_mrr_at_100_std
value: 10.710600000000001
- type: nauc_mrr_at_100_diff1
value: 17.4312
- type: nauc_mrr_at_1000_max
value: 27.172600000000003
- type: nauc_mrr_at_1000_std
value: 10.6434
- type: nauc_mrr_at_1000_diff1
value: 17.421400000000002
- type: main_score
value: 17.380000000000003
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: pearson
value: 75.385
- type: spearman
value: 68.46560000000001
- type: cosine_pearson
value: 75.385
- type: cosine_spearman
value: 68.46560000000001
- type: manhattan_pearson
value: 72.53309999999999
- type: manhattan_spearman
value: 68.79899999999999
- type: euclidean_pearson
value: 72.5239
- type: euclidean_spearman
value: 68.46560000000001
- type: main_score
value: 68.46560000000001
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: pearson
value: 80.9088
- type: spearman
value: 74.7362
- type: cosine_pearson
value: 80.9088
- type: cosine_spearman
value: 74.7362
- type: manhattan_pearson
value: 77.3291
- type: manhattan_spearman
value: 75.0881
- type: euclidean_pearson
value: 77.5321
- type: euclidean_spearman
value: 74.7347
- type: main_score
value: 74.7362
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: pearson
value: 74.6345
- type: spearman
value: 75.63990000000001
- type: cosine_pearson
value: 74.6345
- type: cosine_spearman
value: 75.63990000000001
- type: manhattan_pearson
value: 75.5227
- type: manhattan_spearman
value: 75.5136
- type: euclidean_pearson
value: 75.5744
- type: euclidean_spearman
value: 75.63990000000001
- type: main_score
value: 75.63990000000001
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 76.66629999999999
- type: spearman
value: 73.1976
- type: cosine_pearson
value: 76.66629999999999
- type: cosine_spearman
value: 73.1976
- type: manhattan_pearson
value: 75.0827
- type: manhattan_spearman
value: 73.2472
- type: euclidean_pearson
value: 75.2873
- type: euclidean_spearman
value: 73.1976
- type: main_score
value: 73.1976
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 84.33810000000001
- type: spearman
value: 85.0551
- type: cosine_pearson
value: 84.33810000000001
- type: cosine_spearman
value: 85.0551
- type: manhattan_pearson
value: 84.5984
- type: manhattan_spearman
value: 85.1619
- type: euclidean_pearson
value: 84.529
- type: euclidean_spearman
value: 85.0551
- type: main_score
value: 85.0551
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 79.5933
- type: spearman
value: 81.11120000000001
- type: cosine_pearson
value: 79.5933
- type: cosine_spearman
value: 81.11120000000001
- type: manhattan_pearson
value: 80.136
- type: manhattan_spearman
value: 80.8767
- type: euclidean_pearson
value: 80.3305
- type: euclidean_spearman
value: 81.11120000000001
- type: main_score
value: 81.11120000000001
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 38.2331
- type: spearman
value: 33.7346
- type: cosine_pearson
value: 38.2331
- type: cosine_spearman
value: 33.7346
- type: manhattan_pearson
value: 40.986
- type: manhattan_spearman
value: 34.253099999999996
- type: euclidean_pearson
value: 40.2622
- type: euclidean_spearman
value: 33.7346
- type: main_score
value: 33.7346
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 73.5477
- type: spearman
value: 74.1745
- type: cosine_pearson
value: 73.5477
- type: cosine_spearman
value: 74.1745
- type: manhattan_pearson
value: 74.84920000000001
- type: manhattan_spearman
value: 74.49900000000001
- type: euclidean_pearson
value: 74.14
- type: euclidean_spearman
value: 74.1745
- type: main_score
value: 74.1745
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 66.7169
- type: spearman
value: 66.864
- type: cosine_pearson
value: 66.7169
- type: cosine_spearman
value: 66.864
- type: manhattan_pearson
value: 67.39359999999999
- type: manhattan_spearman
value: 67.0985
- type: euclidean_pearson
value: 66.9389
- type: euclidean_spearman
value: 66.864
- type: main_score
value: 66.864
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 70.5101
- type: spearman
value: 70.05930000000001
- type: cosine_pearson
value: 70.5101
- type: cosine_spearman
value: 70.05930000000001
- type: manhattan_pearson
value: 72.7524
- type: manhattan_spearman
value: 71.2907
- type: euclidean_pearson
value: 71.148
- type: euclidean_spearman
value: 70.05930000000001
- type: main_score
value: 70.05930000000001
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 68.3089
- type: spearman
value: 68.4899
- type: cosine_pearson
value: 68.3089
- type: cosine_spearman
value: 68.4899
- type: manhattan_pearson
value: 69.3956
- type: manhattan_spearman
value: 68.9486
- type: euclidean_pearson
value: 68.8059
- type: euclidean_spearman
value: 68.4899
- type: main_score
value: 68.4899
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 78.28739999999999
- type: spearman
value: 78.6966
- type: cosine_pearson
value: 78.28739999999999
- type: cosine_spearman
value: 78.6966
- type: manhattan_pearson
value: 78.97070000000001
- type: manhattan_spearman
value: 79.1907
- type: euclidean_pearson
value: 78.36070000000001
- type: euclidean_spearman
value: 78.6966
- type: main_score
value: 78.6966
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 59.611999999999995
- type: spearman
value: 59.9288
- type: cosine_pearson
value: 59.611999999999995
- type: cosine_spearman
value: 59.9288
- type: manhattan_pearson
value: 60.3549
- type: manhattan_spearman
value: 59.696099999999994
- type: euclidean_pearson
value: 60.4754
- type: euclidean_spearman
value: 59.9288
- type: main_score
value: 59.9288
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 70.6341
- type: spearman
value: 69.9775
- type: cosine_pearson
value: 70.6341
- type: cosine_spearman
value: 69.9775
- type: manhattan_pearson
value: 72.7788
- type: manhattan_spearman
value: 71.2033
- type: euclidean_pearson
value: 71.5822
- type: euclidean_spearman
value: 69.9775
- type: main_score
value: 69.9775
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 67.2703
- type: spearman
value: 67.58229999999999
- type: cosine_pearson
value: 67.2703
- type: cosine_spearman
value: 67.58229999999999
- type: manhattan_pearson
value: 68.1768
- type: manhattan_spearman
value: 67.6479
- type: euclidean_pearson
value: 67.9708
- type: euclidean_spearman
value: 67.58229999999999
- type: main_score
value: 67.58229999999999
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 62.2109
- type: spearman
value: 56.2314
- type: cosine_pearson
value: 62.2109
- type: cosine_spearman
value: 56.2314
- type: manhattan_pearson
value: 65.9455
- type: manhattan_spearman
value: 56.5496
- type: euclidean_pearson
value: 65.30550000000001
- type: euclidean_spearman
value: 56.2314
- type: main_score
value: 56.2314
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 74.4185
- type: spearman
value: 72.82119999999999
- type: cosine_pearson
value: 74.4185
- type: cosine_spearman
value: 72.82119999999999
- type: manhattan_pearson
value: 75.6921
- type: manhattan_spearman
value: 72.3315
- type: euclidean_pearson
value: 75.1725
- type: euclidean_spearman
value: 72.82119999999999
- type: main_score
value: 72.82119999999999
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 78.6974
- type: spearman
value: 79.5845
- type: cosine_pearson
value: 78.6974
- type: cosine_spearman
value: 79.5845
- type: manhattan_pearson
value: 79.6724
- type: manhattan_spearman
value: 79.668
- type: euclidean_pearson
value: 79.69380000000001
- type: euclidean_spearman
value: 79.5845
- type: main_score
value: 79.5845
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 71.3237
- type: spearman
value: 71.5178
- type: cosine_pearson
value: 71.3237
- type: cosine_spearman
value: 71.5178
- type: manhattan_pearson
value: 73.3948
- type: manhattan_spearman
value: 71.5607
- type: euclidean_pearson
value: 73.1403
- type: euclidean_spearman
value: 71.5178
- type: main_score
value: 71.5178
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 75.5279
- type: spearman
value: 76.9844
- type: cosine_pearson
value: 75.5279
- type: cosine_spearman
value: 76.9844
- type: manhattan_pearson
value: 77.5474
- type: manhattan_spearman
value: 77.4353
- type: euclidean_pearson
value: 77.1612
- type: euclidean_spearman
value: 76.9844
- type: main_score
value: 76.9844
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 79.33109999999999
- type: mrr
value: 94.0725
- type: nAUC_map_max
value: 59.0089
- type: nAUC_map_std
value: 69.9131
- type: nAUC_map_diff1
value: 5.900600000000001
- type: nAUC_mrr_max
value: 84.5132
- type: nAUC_mrr_std
value: 77.767
- type: nAUC_mrr_diff1
value: 46.5557
- type: main_score
value: 79.33109999999999
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_3
value: 57.781000000000006
- type: ndcg_at_5
value: 60.925
- type: ndcg_at_10
value: 63.254
- type: ndcg_at_20
value: 64.955
- type: ndcg_at_100
value: 66.155
- type: ndcg_at_1000
value: 67.193
- type: map_at_1
value: 48.428
- type: map_at_3
value: 55.145999999999994
- type: map_at_5
value: 57.055
- type: map_at_10
value: 58.17
- type: map_at_20
value: 58.723000000000006
- type: map_at_100
value: 58.901
- type: map_at_1000
value: 58.940000000000005
- type: recall_at_1
value: 48.428
- type: recall_at_3
value: 62.55
- type: recall_at_5
value: 70.367
- type: recall_at_10
value: 76.972
- type: recall_at_20
value: 83.317
- type: recall_at_100
value: 89.7
- type: recall_at_1000
value: 98.0
- type: precision_at_1
value: 51.333
- type: precision_at_3
value: 22.444
- type: precision_at_5
value: 15.4
- type: precision_at_10
value: 8.6
- type: precision_at_20
value: 4.717
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.11100000000000002
- type: mrr_at_1
value: 51.3333
- type: mrr_at_3
value: 57.5556
- type: mrr_at_5
value: 59.255599999999994
- type: mrr_at_10
value: 60.104400000000005
- type: mrr_at_20
value: 60.4592
- type: mrr_at_100
value: 60.590999999999994
- type: mrr_at_1000
value: 60.622299999999996
- type: nauc_ndcg_at_1_max
value: 55.0684
- type: nauc_ndcg_at_1_std
value: 13.461200000000002
- type: nauc_ndcg_at_1_diff1
value: 67.4931
- type: nauc_ndcg_at_3_max
value: 54.1942
- type: nauc_ndcg_at_3_std
value: 11.029300000000001
- type: nauc_ndcg_at_3_diff1
value: 61.4423
- type: nauc_ndcg_at_5_max
value: 53.712199999999996
- type: nauc_ndcg_at_5_std
value: 11.0586
- type: nauc_ndcg_at_5_diff1
value: 59.3723
- type: nauc_ndcg_at_10_max
value: 55.2513
- type: nauc_ndcg_at_10_std
value: 13.413400000000001
- type: nauc_ndcg_at_10_diff1
value: 58.5176
- type: nauc_ndcg_at_20_max
value: 56.721900000000005
- type: nauc_ndcg_at_20_std
value: 14.9832
- type: nauc_ndcg_at_20_diff1
value: 59.1445
- type: nauc_ndcg_at_100_max
value: 56.5049
- type: nauc_ndcg_at_100_std
value: 15.021799999999999
- type: nauc_ndcg_at_100_diff1
value: 59.4117
- type: nauc_ndcg_at_1000_max
value: 56.0829
- type: nauc_ndcg_at_1000_std
value: 14.4429
- type: nauc_ndcg_at_1000_diff1
value: 60.45700000000001
- type: nauc_map_at_1_max
value: 50.901799999999994
- type: nauc_map_at_1_std
value: 6.0093
- type: nauc_map_at_1_diff1
value: 66.6214
- type: nauc_map_at_3_max
value: 52.684200000000004
- type: nauc_map_at_3_std
value: 7.9088
- type: nauc_map_at_3_diff1
value: 62.906600000000005
- type: nauc_map_at_5_max
value: 52.6187
- type: nauc_map_at_5_std
value: 8.2372
- type: nauc_map_at_5_diff1
value: 61.772000000000006
- type: nauc_map_at_10_max
value: 53.317899999999995
- type: nauc_map_at_10_std
value: 9.397
- type: nauc_map_at_10_diff1
value: 61.355599999999995
- type: nauc_map_at_20_max
value: 54.04259999999999
- type: nauc_map_at_20_std
value: 10.2201
- type: nauc_map_at_20_diff1
value: 61.684000000000005
- type: nauc_map_at_100_max
value: 54.0394
- type: nauc_map_at_100_std
value: 10.2894
- type: nauc_map_at_100_diff1
value: 61.7302
- type: nauc_map_at_1000_max
value: 54.024300000000004
- type: nauc_map_at_1000_std
value: 10.2881
- type: nauc_map_at_1000_diff1
value: 61.7661
- type: nauc_recall_at_1_max
value: 50.901799999999994
- type: nauc_recall_at_1_std
value: 6.0093
- type: nauc_recall_at_1_diff1
value: 66.6214
- type: nauc_recall_at_3_max
value: 52.8806
- type: nauc_recall_at_3_std
value: 10.7463
- type: nauc_recall_at_3_diff1
value: 55.5486
- type: nauc_recall_at_5_max
value: 52.277300000000004
- type: nauc_recall_at_5_std
value: 12.2395
- type: nauc_recall_at_5_diff1
value: 49.147800000000004
- type: nauc_recall_at_10_max
value: 57.403499999999994
- type: nauc_recall_at_10_std
value: 20.4581
- type: nauc_recall_at_10_diff1
value: 44.0595
- type: nauc_recall_at_20_max
value: 65.5378
- type: nauc_recall_at_20_std
value: 29.5288
- type: nauc_recall_at_20_diff1
value: 43.2217
- type: nauc_recall_at_100_max
value: 67.4941
- type: nauc_recall_at_100_std
value: 36.178399999999996
- type: nauc_recall_at_100_diff1
value: 39.3443
- type: nauc_recall_at_1000_max
value: 72.50229999999999
- type: nauc_recall_at_1000_std
value: 51.455
- type: nauc_recall_at_1000_diff1
value: 62.153800000000004
- type: nauc_precision_at_1_max
value: 55.0684
- type: nauc_precision_at_1_std
value: 13.461200000000002
- type: nauc_precision_at_1_diff1
value: 67.4931
- type: nauc_precision_at_3_max
value: 54.947599999999994
- type: nauc_precision_at_3_std
value: 23.1875
- type: nauc_precision_at_3_diff1
value: 51.166199999999996
- type: nauc_precision_at_5_max
value: 50.1483
- type: nauc_precision_at_5_std
value: 27.1119
- type: nauc_precision_at_5_diff1
value: 37.3846
- type: nauc_precision_at_10_max
value: 46.800799999999995
- type: nauc_precision_at_10_std
value: 37.737500000000004
- type: nauc_precision_at_10_diff1
value: 22.945999999999998
- type: nauc_precision_at_20_max
value: 43.980000000000004
- type: nauc_precision_at_20_std
value: 46.3352
- type: nauc_precision_at_20_diff1
value: 14.718300000000001
- type: nauc_precision_at_100_max
value: 34.8346
- type: nauc_precision_at_100_std
value: 49.0032
- type: nauc_precision_at_100_diff1
value: 4.7538
- type: nauc_precision_at_1000_max
value: 19.9994
- type: nauc_precision_at_1000_std
value: 51.132999999999996
- type: nauc_precision_at_1000_diff1
value: -6.5839
- type: nauc_mrr_at_1_max
value: 55.0684
- type: nauc_mrr_at_1_std
value: 13.461200000000002
- type: nauc_mrr_at_1_diff1
value: 67.4931
- type: nauc_mrr_at_3_max
value: 56.2153
- type: nauc_mrr_at_3_std
value: 15.4146
- type: nauc_mrr_at_3_diff1
value: 63.273199999999996
- type: nauc_mrr_at_5_max
value: 56.0011
- type: nauc_mrr_at_5_std
value: 15.7535
- type: nauc_mrr_at_5_diff1
value: 62.1466
- type: nauc_mrr_at_10_max
value: 56.643100000000004
- type: nauc_mrr_at_10_std
value: 16.354
- type: nauc_mrr_at_10_diff1
value: 62.0124
- type: nauc_mrr_at_20_max
value: 56.686800000000005
- type: nauc_mrr_at_20_std
value: 16.1984
- type: nauc_mrr_at_20_diff1
value: 62.095
- type: nauc_mrr_at_100_max
value: 56.6659
- type: nauc_mrr_at_100_std
value: 16.1601
- type: nauc_mrr_at_100_diff1
value: 62.157399999999996
- type: nauc_mrr_at_1000_max
value: 56.657599999999995
- type: nauc_mrr_at_1000_std
value: 16.1579
- type: nauc_mrr_at_1000_diff1
value: 62.195
- type: main_score
value: 63.254
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.7465
- type: similarity_accuracy_threshold
value: 84.08489999999999
- type: similarity_f1
value: 86.9388
- type: similarity_f1_threshold
value: 84.08489999999999
- type: similarity_precision
value: 88.75
- type: similarity_recall
value: 85.2
- type: similarity_ap
value: 93.56139999999999
- type: cosine_accuracy
value: 99.7465
- type: cosine_accuracy_threshold
value: 84.08489999999999
- type: cosine_f1
value: 86.9388
- type: cosine_f1_threshold
value: 84.08489999999999
- type: cosine_precision
value: 88.75
- type: cosine_recall
value: 85.2
- type: cosine_ap
value: 93.56139999999999
- type: manhattan_accuracy
value: 99.7614
- type: manhattan_accuracy_threshold
value: 853.1299
- type: manhattan_f1
value: 87.7053
- type: manhattan_f1_threshold
value: 888.5799999999999
- type: manhattan_precision
value: 87.3142
- type: manhattan_recall
value: 88.1
- type: manhattan_ap
value: 94.0777
- type: euclidean_accuracy
value: 99.7465
- type: euclidean_accuracy_threshold
value: 56.4183
- type: euclidean_f1
value: 86.9388
- type: euclidean_f1_threshold
value: 56.4183
- type: euclidean_precision
value: 88.75
- type: euclidean_recall
value: 85.2
- type: euclidean_ap
value: 93.5613
- type: dot_accuracy
value: 99.7465
- type: dot_accuracy_threshold
value: 84.08489999999999
- type: dot_f1
value: 86.9388
- type: dot_f1_threshold
value: 84.08489999999999
- type: dot_precision
value: 88.75
- type: dot_recall
value: 85.2
- type: dot_ap
value: 93.56139999999999
- type: max_accuracy
value: 99.7614
- type: max_f1
value: 87.7053
- type: max_precision
value: 88.75
- type: max_recall
value: 88.1
- type: max_ap
value: 94.0777
- type: main_score
value: 94.0777
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 54.13980000000001
- type: v_measure_std
value: 5.5665
- type: main_score
value: 54.13980000000001
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 32.6113
- type: v_measure_std
value: 1.6389999999999998
- type: main_score
value: 32.6113
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 50.813900000000004
- type: mrr
value: 51.702099999999994
- type: nAUC_map_max
value: 14.127600000000001
- type: nAUC_map_std
value: 8.6735
- type: nAUC_map_diff1
value: 36.4317
- type: nAUC_mrr_max
value: 15.504399999999999
- type: nAUC_mrr_std
value: 9.7053
- type: nAUC_mrr_diff1
value: 36.7021
- type: main_score
value: 50.813900000000004
task:
type: Reranking
- dataset:
config: default
name: MTEB StackOverflowQA (default)
revision: db8f169f3894c14a00251061f957b2063eef2bd5
split: test
type: CoIR-Retrieval/stackoverflow-qa
metrics:
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value: 54.26299999999999
- type: ndcg_at_3
value: 62.395
- type: ndcg_at_5
value: 64.603
- type: ndcg_at_10
value: 66.57600000000001
- type: ndcg_at_20
value: 68.089
- type: ndcg_at_100
value: 69.587
- type: ndcg_at_1000
value: 70.216
- type: map_at_1
value: 54.26299999999999
- type: map_at_3
value: 60.373
- type: map_at_5
value: 61.609
- type: map_at_10
value: 62.419999999999995
- type: map_at_20
value: 62.83800000000001
- type: map_at_100
value: 63.04
- type: map_at_1000
value: 63.063
- type: recall_at_1
value: 54.26299999999999
- type: recall_at_3
value: 68.255
- type: recall_at_5
value: 73.571
- type: recall_at_10
value: 79.689
- type: recall_at_20
value: 85.65700000000001
- type: recall_at_100
value: 93.781
- type: recall_at_1000
value: 98.79599999999999
- type: precision_at_1
value: 54.26299999999999
- type: precision_at_3
value: 22.752
- type: precision_at_5
value: 14.713999999999999
- type: precision_at_10
value: 7.968999999999999
- type: precision_at_20
value: 4.283
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.099
- type: mrr_at_1
value: 54.2628
- type: mrr_at_3
value: 60.372800000000005
- type: mrr_at_5
value: 61.609
- type: mrr_at_10
value: 62.4202
- type: mrr_at_20
value: 62.83800000000001
- type: mrr_at_100
value: 63.0402
- type: mrr_at_1000
value: 63.06270000000001
- type: nauc_ndcg_at_1_max
value: 61.3558
- type: nauc_ndcg_at_1_std
value: -7.5783000000000005
- type: nauc_ndcg_at_1_diff1
value: 72.637
- type: nauc_ndcg_at_3_max
value: 59.621900000000004
- type: nauc_ndcg_at_3_std
value: -7.8752
- type: nauc_ndcg_at_3_diff1
value: 67.341
- type: nauc_ndcg_at_5_max
value: 59.32150000000001
- type: nauc_ndcg_at_5_std
value: -6.783500000000001
- type: nauc_ndcg_at_5_diff1
value: 66.3908
- type: nauc_ndcg_at_10_max
value: 58.8665
- type: nauc_ndcg_at_10_std
value: -6.8839999999999995
- type: nauc_ndcg_at_10_diff1
value: 65.5914
- type: nauc_ndcg_at_20_max
value: 59.071
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value: -6.7216
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value: 66.0076
- type: nauc_ndcg_at_100_max
value: 59.2928
- type: nauc_ndcg_at_100_std
value: -6.0869
- type: nauc_ndcg_at_100_diff1
value: 66.5509
- type: nauc_ndcg_at_1000_max
value: 59.551
- type: nauc_ndcg_at_1000_std
value: -6.3229
- type: nauc_ndcg_at_1000_diff1
value: 67.0501
- type: nauc_map_at_1_max
value: 61.3558
- type: nauc_map_at_1_std
value: -7.5783000000000005
- type: nauc_map_at_1_diff1
value: 72.637
- type: nauc_map_at_3_max
value: 60.0638
- type: nauc_map_at_3_std
value: -7.824599999999999
- type: nauc_map_at_3_diff1
value: 68.7255
- type: nauc_map_at_5_max
value: 59.9035
- type: nauc_map_at_5_std
value: -7.236199999999999
- type: nauc_map_at_5_diff1
value: 68.2474
- type: nauc_map_at_10_max
value: 59.73159999999999
- type: nauc_map_at_10_std
value: -7.3129
- type: nauc_map_at_10_diff1
value: 67.9742
- type: nauc_map_at_20_max
value: 59.799800000000005
- type: nauc_map_at_20_std
value: -7.2599
- type: nauc_map_at_20_diff1
value: 68.1128
- type: nauc_map_at_100_max
value: 59.8324
- type: nauc_map_at_100_std
value: -7.1589
- type: nauc_map_at_100_diff1
value: 68.1784
- type: nauc_map_at_1000_max
value: 59.845099999999995
- type: nauc_map_at_1000_std
value: -7.1592
- type: nauc_map_at_1000_diff1
value: 68.19770000000001
- type: nauc_recall_at_1_max
value: 61.3558
- type: nauc_recall_at_1_std
value: -7.5783000000000005
- type: nauc_recall_at_1_diff1
value: 72.637
- type: nauc_recall_at_3_max
value: 58.1732
- type: nauc_recall_at_3_std
value: -8.028599999999999
- type: nauc_recall_at_3_diff1
value: 62.7847
- type: nauc_recall_at_5_max
value: 57.1488
- type: nauc_recall_at_5_std
value: -4.9189
- type: nauc_recall_at_5_diff1
value: 59.392599999999995
- type: nauc_recall_at_10_max
value: 54.7384
- type: nauc_recall_at_10_std
value: -4.683
- type: nauc_recall_at_10_diff1
value: 54.317499999999995
- type: nauc_recall_at_20_max
value: 54.5659
- type: nauc_recall_at_20_std
value: -2.9657
- type: nauc_recall_at_20_diff1
value: 53.039899999999996
- type: nauc_recall_at_100_max
value: 53.5805
- type: nauc_recall_at_100_std
value: 12.822
- type: nauc_recall_at_100_diff1
value: 49.3168
- type: nauc_recall_at_1000_max
value: 64.52839999999999
- type: nauc_recall_at_1000_std
value: 44.954699999999995
- type: nauc_recall_at_1000_diff1
value: 51.3607
- type: nauc_precision_at_1_max
value: 61.3558
- type: nauc_precision_at_1_std
value: -7.5783000000000005
- type: nauc_precision_at_1_diff1
value: 72.637
- type: nauc_precision_at_3_max
value: 58.1732
- type: nauc_precision_at_3_std
value: -8.028599999999999
- type: nauc_precision_at_3_diff1
value: 62.7847
- type: nauc_precision_at_5_max
value: 57.1488
- type: nauc_precision_at_5_std
value: -4.9189
- type: nauc_precision_at_5_diff1
value: 59.392599999999995
- type: nauc_precision_at_10_max
value: 54.7384
- type: nauc_precision_at_10_std
value: -4.683
- type: nauc_precision_at_10_diff1
value: 54.317499999999995
- type: nauc_precision_at_20_max
value: 54.5659
- type: nauc_precision_at_20_std
value: -2.9657
- type: nauc_precision_at_20_diff1
value: 53.039899999999996
- type: nauc_precision_at_100_max
value: 53.5805
- type: nauc_precision_at_100_std
value: 12.822
- type: nauc_precision_at_100_diff1
value: 49.3168
- type: nauc_precision_at_1000_max
value: 64.52839999999999
- type: nauc_precision_at_1000_std
value: 44.954699999999995
- type: nauc_precision_at_1000_diff1
value: 51.3607
- type: nauc_mrr_at_1_max
value: 61.3558
- type: nauc_mrr_at_1_std
value: -7.5783000000000005
- type: nauc_mrr_at_1_diff1
value: 72.637
- type: nauc_mrr_at_3_max
value: 60.0638
- type: nauc_mrr_at_3_std
value: -7.824599999999999
- type: nauc_mrr_at_3_diff1
value: 68.7255
- type: nauc_mrr_at_5_max
value: 59.9035
- type: nauc_mrr_at_5_std
value: -7.236199999999999
- type: nauc_mrr_at_5_diff1
value: 68.2474
- type: nauc_mrr_at_10_max
value: 59.73159999999999
- type: nauc_mrr_at_10_std
value: -7.3129
- type: nauc_mrr_at_10_diff1
value: 67.9742
- type: nauc_mrr_at_20_max
value: 59.799800000000005
- type: nauc_mrr_at_20_std
value: -7.2599
- type: nauc_mrr_at_20_diff1
value: 68.1128
- type: nauc_mrr_at_100_max
value: 59.8324
- type: nauc_mrr_at_100_std
value: -7.1589
- type: nauc_mrr_at_100_diff1
value: 68.1784
- type: nauc_mrr_at_1000_max
value: 59.845099999999995
- type: nauc_mrr_at_1000_std
value: -7.1592
- type: nauc_mrr_at_1000_diff1
value: 68.19770000000001
- type: main_score
value: 66.57600000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: pearson
value: 31.255699999999997
- type: spearman
value: 31.121
- type: cosine_spearman
value: 31.121
- type: cosine_pearson
value: 31.255699999999997
- type: dot_spearman
value: 31.121
- type: dot_pearson
value: 31.255699999999997
- type: main_score
value: 31.121
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntheticText2SQL (default)
revision: 686b87296c3a0191b5d9415a00526c62db9fce09
split: test
type: CoIR-Retrieval/synthetic-text2sql
metrics:
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value: 2.752
- type: ndcg_at_3
value: 32.669
- type: ndcg_at_5
value: 36.313
- type: ndcg_at_10
value: 39.341
- type: ndcg_at_20
value: 41.22
- type: ndcg_at_100
value: 43.682
- type: ndcg_at_1000
value: 44.679
- type: map_at_1
value: 2.752
- type: map_at_3
value: 25.918999999999997
- type: map_at_5
value: 27.939000000000004
- type: map_at_10
value: 29.195999999999998
- type: map_at_20
value: 29.711
- type: map_at_100
value: 30.057000000000002
- type: map_at_1000
value: 30.092999999999996
- type: recall_at_1
value: 2.752
- type: recall_at_3
value: 51.957
- type: recall_at_5
value: 60.809999999999995
- type: recall_at_10
value: 70.14200000000001
- type: recall_at_20
value: 77.576
- type: recall_at_100
value: 90.771
- type: recall_at_1000
value: 98.667
- type: precision_at_1
value: 2.752
- type: precision_at_3
value: 17.319000000000003
- type: precision_at_5
value: 12.162
- type: precision_at_10
value: 7.013999999999999
- type: precision_at_20
value: 3.879
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.099
- type: mrr_at_1
value: 23.534399999999998
- type: mrr_at_3
value: 37.8739
- type: mrr_at_5
value: 39.6078
- type: mrr_at_10
value: 40.7592
- type: mrr_at_20
value: 41.2449
- type: mrr_at_100
value: 41.5832
- type: mrr_at_1000
value: 41.6198
- type: nauc_ndcg_at_1_max
value: 13.625200000000001
- type: nauc_ndcg_at_1_std
value: -17.2342
- type: nauc_ndcg_at_1_diff1
value: 72.20830000000001
- type: nauc_ndcg_at_3_max
value: 33.5059
- type: nauc_ndcg_at_3_std
value: -15.198400000000001
- type: nauc_ndcg_at_3_diff1
value: -55.0763
- type: nauc_ndcg_at_5_max
value: 31.461699999999997
- type: nauc_ndcg_at_5_std
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
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- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_1000_max
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name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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type: mteb/touche2020
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
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task:
type: Classification
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config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
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task:
type: Classification
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config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
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task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
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- type: similarity_precision
value: 52.937400000000004
- type: similarity_recall
value: 65.1451
- type: similarity_ap
value: 61.6413
- type: cosine_accuracy
value: 82.3866
- type: cosine_accuracy_threshold
value: 87.0467
- type: cosine_f1
value: 58.4102
- type: cosine_f1_threshold
value: 82.61540000000001
- type: cosine_precision
value: 52.937400000000004
- type: cosine_recall
value: 65.1451
- type: cosine_ap
value: 61.6413
- type: manhattan_accuracy
value: 82.12429999999999
- type: manhattan_accuracy_threshold
value: 786.2048
- type: manhattan_f1
value: 57.862899999999996
- type: manhattan_f1_threshold
value: 911.9348
- type: manhattan_precision
value: 50.2725
- type: manhattan_recall
value: 68.15299999999999
- type: manhattan_ap
value: 60.6893
- type: euclidean_accuracy
value: 82.3866
- type: euclidean_accuracy_threshold
value: 50.8985
- type: euclidean_f1
value: 58.4102
- type: euclidean_f1_threshold
value: 58.9654
- type: euclidean_precision
value: 52.937400000000004
- type: euclidean_recall
value: 65.1451
- type: euclidean_ap
value: 61.6413
- type: dot_accuracy
value: 82.3866
- type: dot_accuracy_threshold
value: 87.0467
- type: dot_f1
value: 58.4102
- type: dot_f1_threshold
value: 82.61540000000001
- type: dot_precision
value: 52.937400000000004
- type: dot_recall
value: 65.1451
- type: dot_ap
value: 61.6413
- type: max_accuracy
value: 82.3866
- type: max_f1
value: 58.4102
- type: max_precision
value: 52.937400000000004
- type: max_recall
value: 68.15299999999999
- type: max_ap
value: 61.6413
- type: main_score
value: 61.6413
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 88.77629999999999
- type: similarity_accuracy_threshold
value: 82.2251
- type: similarity_f1
value: 77.3613
- type: similarity_f1_threshold
value: 80.3174
- type: similarity_precision
value: 75.0906
- type: similarity_recall
value: 79.7736
- type: similarity_ap
value: 85.6694
- type: cosine_accuracy
value: 88.77629999999999
- type: cosine_accuracy_threshold
value: 82.2251
- type: cosine_f1
value: 77.3613
- type: cosine_f1_threshold
value: 80.3174
- type: cosine_precision
value: 75.0906
- type: cosine_recall
value: 79.7736
- type: cosine_ap
value: 85.6694
- type: manhattan_accuracy
value: 88.7317
- type: manhattan_accuracy_threshold
value: 914.4955
- type: manhattan_f1
value: 77.1707
- type: manhattan_f1_threshold
value: 946.5603
- type: manhattan_precision
value: 76.2825
- type: manhattan_recall
value: 78.0798
- type: manhattan_ap
value: 85.5718
- type: euclidean_accuracy
value: 88.77629999999999
- type: euclidean_accuracy_threshold
value: 59.6237
- type: euclidean_f1
value: 77.3613
- type: euclidean_f1_threshold
value: 62.7417
- type: euclidean_precision
value: 75.0906
- type: euclidean_recall
value: 79.7736
- type: euclidean_ap
value: 85.6694
- type: dot_accuracy
value: 88.77629999999999
- type: dot_accuracy_threshold
value: 82.2251
- type: dot_f1
value: 77.3613
- type: dot_f1_threshold
value: 80.3174
- type: dot_precision
value: 75.0906
- type: dot_recall
value: 79.7736
- type: dot_ap
value: 85.6694
- type: max_accuracy
value: 88.77629999999999
- type: max_f1
value: 77.3613
- type: max_precision
value: 76.2825
- type: max_recall
value: 79.7736
- type: max_ap
value: 85.6694
- type: main_score
value: 85.6694
task:
type: PairClassification
pipeline_tag: sentence-similarity
---
# Granite-Embedding-107m-multilingual
**Model Summary:**
Granite-Embedding-107M-Multilingual is a 107M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using contrastive finetuning, knowledge distillation and model merging for improved performance.
- **Developers:** Granite Embedding Team, IBM
- **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Technical Report](https://arxiv.org/abs/2502.20204)
- **Release Date**: December 18th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Languages:**
English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite-Embedding-107M-Multilingual for languages beyond these 12 languages.
**Intended use:**
The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
**Usage with Sentence Transformers:**
The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
```shell
pip install sentence_transformers
```
The model can then be used to encode pairs of text and find the similarity between their representations
```python
from sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-107m-multilingual"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
input_passages = [
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
```
**Usage with Huggingface Transformers:**
This is a simple example of how to use the Granite-Embedding-107m-Multilingual model with the Transformers library and PyTorch.
First, install the required libraries
```shell
pip install transformers torch
```
The model can then be used to encode pairs of text
```python
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-107m-multilingual"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode queries
with torch.no_grad():
# Queries
model_output = model(**tokenized_queries)
# Perform pooling. granite-embedding-107m-multilingual uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
The average performance of the Granite-Embedding-107M-Multilingual on Multilingual Miracl (across 18 langauges), Mintaka Retrieval (across 8 languages) and MTEB Retrieval for English (across 15 tasks), German (across 4 tasks), Spanish (across 2 tasks), Frenc (across 5 tasks), Japanese (across 2 tasks), Arabic (1 task), Korean (1 task) and Chinese (across 8 tasks) is reported below. Granite-Embedding-107M-Multilingual is twice as fast as other models with similar embedding dimensions.
| Model | Paramters (M)| Embedding Dimension | Miracl (18) | Mintaka Retrieval (8) | MTEB English (15) | MTEB German (4) |MTEB Spanish (2) | MTEB French (5) | MTEB Japanese (2) | MTEB Arabic (1) | MTEB Korean (1) | MTEB Chinese (8) |
|------------------------------------|:------------:|:-------------------:|:-------------:| :---------------------:|:-----------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|----------------:|-----------------:|
|granite-embedding-107m-multilingual | 107 | 384 | 55.9 | 22.6 | 45.3 | 70.3 | 48.7 | 51.1 | 59.0 | 63.2 | 70.5 | 40.8 |
**Model Architecture:**
Granite-Embedding-107m-Multilingual is based on an encoder-only XLM-RoBERTa like transformer architecture, trained internally at IBM Research.
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
| :--------- | :-------:| :--------: | :---------:| :-----:|
| Embedding size | 384 | 768 | **384** | 768 |
| Number of layers | 6 | 12 | **6** | 12 |
| Number of attention heads | 12 | 12 | **12** | 12 |
| Intermediate size | 1536 | 3072 | **1536** | 3072 |
| Activation Function | GeLU | GeLU | **GeLU** | GeLU |
| Vocabulary Size | 50265 | 50265 | **250002** | 250002 |
| Max. Sequence Length | 512 | 512 | **512** | 512 |
| # Parameters | 30M | 125M | **107M** | 278M |
**Training Data:**
Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
| **Dataset** | **Num. Pairs** |
|:--------------------------------------------------------------------------|:--------------:|
| Multilingual MC4 | 52,823,484 |
| Multilingual Webhose | 12,369,322 |
| English Wikipedia | 20,745,403 |
| Multilingual Wikimedia | 2,911,090 |
| Miracl Corpus (Title-Body) | 10,120,398 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Machine Translations of Stack Exchange Duplicate questions (titles) | 187,195 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Machine Translations of Stack Exchange (Title+Body, Answer) pairs | 1,827,15 |
| SearchQA | 582,261 |
| S2ORC (Title, Abstract) | 41,769,185 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| CCNews | 614,664 |
| XSum | 226,711 |
| SimpleWiki | 102,225 |
| Machine Translated Cross Lingual Parallel Corpora | 28,376,115 |
| SPECTER citation triplets | 684,100 |
| Machine Translations of SPECTER citation triplets | 4,104,600 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| PubMed | 20,000,000 |
| Multilingual Miracl Triples | 81,409 |
| Multilingual MrTydi Triples | 48,715 |
| Sadeeem Question Asnwering | 4,037 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: English Query-Wikipedia Passage | 1,879,093 |
| Synthetic: English Fact Verification | 9,888 |
| Synthetic: Multilingual Query-Wikipedia Passage | 300,266 |
| Synthetic: Multilingual News Summaries | 37,489 |
| IBM Internal Triples | 40,290 |
| IBM Internal Title-Body Pairs | 1,524,586 |
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
**Infrastructure:**
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
**Ethical Considerations and Limitations:**
The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-107m-Multilingual is finetuned on 12 languages, and has a context length of 512 tokens (longer texts will be truncated to this size).
**Resources**
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
<!-- ## Citation
```
@misc{granite-embedding-models,
author = {author 1, author2, ...},
title = {},
journal = {},
volume = {},
year = {2024},
url = {https://arxiv.org/abs/0000.00000},
}
``` -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755565728
|
lisaozill03
| 2025-08-19T01:33:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:33:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibm-granite/granite-embedding-125m-english
|
ibm-granite
| 2025-08-19T01:31:48Z | 49,603 | 14 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"roberta",
"feature-extraction",
"language",
"granite",
"embeddings",
"mteb",
"transformers",
"sentence-similarity",
"en",
"arxiv:2502.20204",
"arxiv:0000.00000",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-12-04T20:28:08Z |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- language
- granite
- embeddings
- mteb
- transformers
model-index:
- name: ibm-granite/granite-embedding-125m-english
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 67.3613
- type: f1
value: 55.0794
- type: f1_weighted
value: 73.55120000000001
- type: ap
value: 17.643900000000002
- type: ap_weighted
value: 17.643900000000002
- type: main_score
value: 67.3613
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 63.403
- type: f1
value: 57.4178
- type: f1_weighted
value: 66.9704
- type: ap
value: 26.892300000000002
- type: ap_weighted
value: 26.892300000000002
- type: main_score
value: 63.403
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 64.5872
- type: f1
value: 64.33330000000001
- type: f1_weighted
value: 64.33330000000001
- type: ap
value: 59.602
- type: ap_weighted
value: 59.602
- type: main_score
value: 64.5872
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 33.534000000000006
- type: f1
value: 32.5389
- type: f1_weighted
value: 32.5389
- type: main_score
value: 33.534000000000006
task:
type: Classification
- dataset:
config: default
name: MTEB AppsRetrieval (default)
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
split: test
type: CoIR-Retrieval/apps
metrics:
- type: ndcg_at_1
value: 6.932
- type: ndcg_at_3
value: 9.577
- type: ndcg_at_5
value: 10.597
- type: ndcg_at_10
value: 11.787
- type: ndcg_at_20
value: 12.863
- type: ndcg_at_100
value: 15.573999999999998
- type: ndcg_at_1000
value: 19.772000000000002
- type: map_at_1
value: 6.932
- type: map_at_3
value: 8.938
- type: map_at_5
value: 9.506
- type: map_at_10
value: 10.0
- type: map_at_20
value: 10.296
- type: map_at_100
value: 10.644
- type: map_at_1000
value: 10.771
- type: recall_at_1
value: 6.932
- type: recall_at_3
value: 11.421000000000001
- type: recall_at_5
value: 13.891
- type: recall_at_10
value: 17.556
- type: recall_at_20
value: 21.806
- type: recall_at_100
value: 36.839
- type: recall_at_1000
value: 71.71300000000001
- type: precision_at_1
value: 6.932
- type: precision_at_3
value: 3.807
- type: precision_at_5
value: 2.778
- type: precision_at_10
value: 1.756
- type: precision_at_20
value: 1.09
- type: precision_at_100
value: 0.368
- type: precision_at_1000
value: 0.07200000000000001
- type: mrr_at_1
value: 6.9323
- type: mrr_at_3
value: 8.9376
- type: mrr_at_5
value: 9.506
- type: mrr_at_10
value: 9.9999
- type: mrr_at_20
value: 10.2957
- type: mrr_at_100
value: 10.643600000000001
- type: mrr_at_1000
value: 10.7707
- type: nauc_ndcg_at_1_max
value: 27.327299999999997
- type: nauc_ndcg_at_1_std
value: 9.6266
- type: nauc_ndcg_at_1_diff1
value: 39.4451
- type: nauc_ndcg_at_3_max
value: 22.9053
- type: nauc_ndcg_at_3_std
value: 10.123
- type: nauc_ndcg_at_3_diff1
value: 27.742099999999997
- type: nauc_ndcg_at_5_max
value: 21.7041
- type: nauc_ndcg_at_5_std
value: 9.661100000000001
- type: nauc_ndcg_at_5_diff1
value: 25.0689
- type: nauc_ndcg_at_10_max
value: 21.0966
- type: nauc_ndcg_at_10_std
value: 10.4106
- type: nauc_ndcg_at_10_diff1
value: 23.4219
- type: nauc_ndcg_at_20_max
value: 20.0575
- type: nauc_ndcg_at_20_std
value: 10.89
- type: nauc_ndcg_at_20_diff1
value: 22.6143
- type: nauc_ndcg_at_100_max
value: 19.4243
- type: nauc_ndcg_at_100_std
value: 11.5431
- type: nauc_ndcg_at_100_diff1
value: 21.013
- type: nauc_ndcg_at_1000_max
value: 20.6057
- type: nauc_ndcg_at_1000_std
value: 13.0027
- type: nauc_ndcg_at_1000_diff1
value: 20.988799999999998
- type: nauc_map_at_1_max
value: 27.327299999999997
- type: nauc_map_at_1_std
value: 9.6266
- type: nauc_map_at_1_diff1
value: 39.4451
- type: nauc_map_at_3_max
value: 23.6991
- type: nauc_map_at_3_std
value: 9.9287
- type: nauc_map_at_3_diff1
value: 29.909799999999997
- type: nauc_map_at_5_max
value: 22.9242
- type: nauc_map_at_5_std
value: 9.640600000000001
- type: nauc_map_at_5_diff1
value: 28.228199999999998
- type: nauc_map_at_10_max
value: 22.612199999999998
- type: nauc_map_at_10_std
value: 10.0051
- type: nauc_map_at_10_diff1
value: 27.3942
- type: nauc_map_at_20_max
value: 22.236
- type: nauc_map_at_20_std
value: 10.168000000000001
- type: nauc_map_at_20_diff1
value: 27.0258
- type: nauc_map_at_100_max
value: 22.1373
- type: nauc_map_at_100_std
value: 10.2741
- type: nauc_map_at_100_diff1
value: 26.717800000000004
- type: nauc_map_at_1000_max
value: 22.1829
- type: nauc_map_at_1000_std
value: 10.3395
- type: nauc_map_at_1000_diff1
value: 26.7158
- type: nauc_recall_at_1_max
value: 27.327299999999997
- type: nauc_recall_at_1_std
value: 9.6266
- type: nauc_recall_at_1_diff1
value: 39.4451
- type: nauc_recall_at_3_max
value: 21.0841
- type: nauc_recall_at_3_std
value: 10.6057
- type: nauc_recall_at_3_diff1
value: 22.745
- type: nauc_recall_at_5_max
value: 19.0389
- type: nauc_recall_at_5_std
value: 9.697899999999999
- type: nauc_recall_at_5_diff1
value: 18.137600000000003
- type: nauc_recall_at_10_max
value: 18.0668
- type: nauc_recall_at_10_std
value: 11.326799999999999
- type: nauc_recall_at_10_diff1
value: 15.423
- type: nauc_recall_at_20_max
value: 15.798100000000002
- type: nauc_recall_at_20_std
value: 12.4585
- type: nauc_recall_at_20_diff1
value: 14.509500000000001
- type: nauc_recall_at_100_max
value: 14.2836
- type: nauc_recall_at_100_std
value: 14.2989
- type: nauc_recall_at_100_diff1
value: 10.7304
- type: nauc_recall_at_1000_max
value: 19.728299999999997
- type: nauc_recall_at_1000_std
value: 24.5691
- type: nauc_recall_at_1000_diff1
value: 6.1472999999999995
- type: nauc_precision_at_1_max
value: 27.327299999999997
- type: nauc_precision_at_1_std
value: 9.6266
- type: nauc_precision_at_1_diff1
value: 39.4451
- type: nauc_precision_at_3_max
value: 21.0841
- type: nauc_precision_at_3_std
value: 10.6057
- type: nauc_precision_at_3_diff1
value: 22.745
- type: nauc_precision_at_5_max
value: 19.0389
- type: nauc_precision_at_5_std
value: 9.697899999999999
- type: nauc_precision_at_5_diff1
value: 18.137600000000003
- type: nauc_precision_at_10_max
value: 18.0668
- type: nauc_precision_at_10_std
value: 11.326799999999999
- type: nauc_precision_at_10_diff1
value: 15.423
- type: nauc_precision_at_20_max
value: 15.798100000000002
- type: nauc_precision_at_20_std
value: 12.4585
- type: nauc_precision_at_20_diff1
value: 14.509500000000001
- type: nauc_precision_at_100_max
value: 14.2836
- type: nauc_precision_at_100_std
value: 14.2989
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task:
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config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
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value: 48.3018
task:
type: Clustering
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config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
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value: 44.837900000000005
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value: 14.089599999999999
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value: 44.837900000000005
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
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- type: mrr
value: 79.3195
- type: nAUC_map_max
value: 23.2658
- type: nAUC_map_std
value: 17.5795
- type: nAUC_map_diff1
value: 11.5539
- type: nAUC_mrr_max
value: 35.565400000000004
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value: 23.7189
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value: 15.962299999999999
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value: 66.4838
task:
type: Reranking
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config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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- type: spearman
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value: 90.1203
- type: cosine_spearman
value: 87.8424
- type: manhattan_pearson
value: 88.1164
- type: manhattan_spearman
value: 87.752
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value: 88.3146
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value: 87.8424
task:
type: STS
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config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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value: 76.9641
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value: 77.9156
task:
type: Classification
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config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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task:
type: Clustering
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config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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value: 36.2911
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task:
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config: python
name: MTEB COIRCodeSearchNetRetrieval (python)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
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config: javascript
name: MTEB COIRCodeSearchNetRetrieval (javascript)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
- dataset:
config: go
name: MTEB COIRCodeSearchNetRetrieval (go)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
type: Retrieval
- dataset:
config: ruby
name: MTEB COIRCodeSearchNetRetrieval (ruby)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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name: MTEB COIRCodeSearchNetRetrieval (java)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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task:
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config: php
name: MTEB COIRCodeSearchNetRetrieval (php)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
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revision: 4885aa143210c98657558c04aaf3dc47cfb54340
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task:
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revision: 160c094312a0e1facb97e55eeddb698c0abe3571
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type: CQADupstackRetrieval_is_a_combined_dataset
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name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
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type: CQADupstackRetrieval_is_a_combined_dataset
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type: mteb/cqadupstack-stats
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task:
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revision: 46989137a86843e03a6195de44b09deda022eec7
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type: mteb/cqadupstack-tex
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task:
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config: javascript
name: MTEB CodeSearchNetCCRetrieval (javascript)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
- dataset:
config: go
name: MTEB CodeSearchNetCCRetrieval (go)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
- dataset:
config: ruby
name: MTEB CodeSearchNetCCRetrieval (ruby)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
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type: CoIR-Retrieval/CodeSearchNet-ccr
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name: MTEB CodeSearchNetCCRetrieval (php)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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task:
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config: go
name: MTEB CodeSearchNetRetrieval (go)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
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revision: 20da4eb20a4b17300c0986ee148c90867a7f2a4d
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task:
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name: MTEB CodeTransOceanDL (default)
revision: 281562cb8a1265ab5c0824bfa6ddcd9b0a15618f
split: test
type: CoIR-Retrieval/codetrans-dl
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task:
type: Retrieval
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config: default
name: MTEB HotpotQA (default)
revision: ab518f4d6fcca38d87c25209f94beba119d02014
split: test
type: mteb/hotpotqa
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification (default)
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
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- type: f1_weighted
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task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO (default)
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
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task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 91.31099999999999
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value: 90.9331
- type: f1_weighted
value: 91.2787
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value: 91.31099999999999
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 54.9362
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value: 38.364399999999996
- type: f1_weighted
value: 57.1133
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value: 54.9362
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.5461
- type: f1
value: 60.8751
- type: f1_weighted
value: 63.248599999999996
- type: main_score
value: 64.5461
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 71.6476
- type: f1
value: 71.03110000000001
- type: f1_weighted
value: 71.3832
- type: main_score
value: 71.6476
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 32.3037
- type: v_measure_std
value: 1.4981
- type: main_score
value: 32.3037
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 31.9128
- type: v_measure_std
value: 1.4597
- type: main_score
value: 31.9128
task:
type: Clustering
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config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
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task:
type: Reranking
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config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
type: Retrieval
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config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
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task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval (default)
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
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value: 37.2354
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value: -33.4342
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value: 77.2283
- type: nauc_mrr_at_3_max
value: 38.000299999999996
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value: -34.9304
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value: 76.20280000000001
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value: 38.3135
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value: -34.707
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value: 76.4365
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value: 38.0013
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value: -34.6562
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value: 76.44069999999999
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value: 38.0368
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value: -34.4726
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value: 76.4482
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value: 38.0243
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value: -34.4696
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value: 76.4569
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value: 38.0227
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value: -34.4733
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value: 76.45739999999999
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value: 87.822
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 54.4296
- type: v_measure_std
value: 5.026400000000001
- type: main_score
value: 54.4296
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P (default)
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 58.1919
- type: v_measure_std
value: 12.618199999999998
- type: main_score
value: 58.1919
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
- type: ndcg_at_1
value: 28.1
- type: ndcg_at_3
value: 22.721
- type: ndcg_at_5
value: 20.015
- type: ndcg_at_10
value: 24.146
- type: ndcg_at_20
value: 27.74
- type: ndcg_at_100
value: 33.900000000000006
- type: ndcg_at_1000
value: 39.728
- type: map_at_1
value: 5.737
- type: map_at_3
value: 10.474
- type: map_at_5
value: 12.656
- type: map_at_10
value: 14.896
- type: map_at_20
value: 16.317999999999998
- type: map_at_100
value: 17.646
- type: map_at_1000
value: 18.029999999999998
- type: recall_at_1
value: 5.737
- type: recall_at_3
value: 12.897
- type: recall_at_5
value: 17.854999999999997
- type: recall_at_10
value: 25.4
- type: recall_at_20
value: 33.817
- type: recall_at_100
value: 53.772
- type: recall_at_1000
value: 82.013
- type: precision_at_1
value: 28.1
- type: precision_at_3
value: 21.2
- type: precision_at_5
value: 17.599999999999998
- type: precision_at_10
value: 12.540000000000001
- type: precision_at_20
value: 8.34
- type: precision_at_100
value: 2.651
- type: precision_at_1000
value: 0.404
- type: mrr_at_1
value: 28.1
- type: mrr_at_3
value: 35.9167
- type: mrr_at_5
value: 38.0967
- type: mrr_at_10
value: 39.578799999999994
- type: mrr_at_20
value: 40.2541
- type: mrr_at_100
value: 40.687
- type: mrr_at_1000
value: 40.722
- type: nauc_ndcg_at_1_max
value: 21.2698
- type: nauc_ndcg_at_1_std
value: 8.8522
- type: nauc_ndcg_at_1_diff1
value: 21.6443
- type: nauc_ndcg_at_3_max
value: 28.6762
- type: nauc_ndcg_at_3_std
value: 13.8129
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value: 16.4517
- type: nauc_ndcg_at_5_max
value: 31.252000000000002
- type: nauc_ndcg_at_5_std
value: 17.3178
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value: 16.8954
- type: nauc_ndcg_at_10_max
value: 32.581700000000005
- type: nauc_ndcg_at_10_std
value: 19.936300000000003
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value: 17.086499999999997
- type: nauc_ndcg_at_20_max
value: 32.3902
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value: 22.8215
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value: 14.6836
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value: 33.2665
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value: 28.93
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value: 14.8837
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value: 32.9079
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value: 28.228900000000003
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value: 15.9599
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value: 20.3725
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value: 8.7546
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value: 20.8754
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value: 27.0845
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value: 12.6727
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value: 15.6365
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value: 15.8701
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value: 15.891
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value: 18.5848
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value: 13.8149
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value: 13.9657
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value: 22.991
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value: 8.7546
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value: 20.8754
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value: 15.5861
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value: 13.9652
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value: 20.4822
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value: 14.566799999999999
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value: 33.9121
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value: 23.4277
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value: 14.5769
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value: 27.683400000000002
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value: 8.519300000000001
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value: 41.281600000000005
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value: 7.3066
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value: 24.2406
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value: 43.2715
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value: 10.2232
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value: 21.2698
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value: 8.8522
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value: 21.6443
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value: 31.2776
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value: 15.8911
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value: 14.357800000000001
- type: nauc_precision_at_5_max
value: 34.034
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value: 20.6595
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value: 15.1316
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value: 34.4474
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value: 23.5843
- type: nauc_precision_at_10_diff1
value: 14.9385
- type: nauc_precision_at_20_max
value: 31.4376
- type: nauc_precision_at_20_std
value: 27.7123
- type: nauc_precision_at_20_diff1
value: 8.6083
- type: nauc_precision_at_100_max
value: 29.401300000000003
- type: nauc_precision_at_100_std
value: 40.5942
- type: nauc_precision_at_100_diff1
value: 7.6172
- type: nauc_precision_at_1000_max
value: 25.2832
- type: nauc_precision_at_1000_std
value: 40.9653
- type: nauc_precision_at_1000_diff1
value: 10.3534
- type: nauc_mrr_at_1_max
value: 21.2698
- type: nauc_mrr_at_1_std
value: 8.8522
- type: nauc_mrr_at_1_diff1
value: 21.6443
- type: nauc_mrr_at_3_max
value: 26.8557
- type: nauc_mrr_at_3_std
value: 12.482600000000001
- type: nauc_mrr_at_3_diff1
value: 19.3542
- type: nauc_mrr_at_5_max
value: 28.0333
- type: nauc_mrr_at_5_std
value: 13.4664
- type: nauc_mrr_at_5_diff1
value: 20.0372
- type: nauc_mrr_at_10_max
value: 28.0659
- type: nauc_mrr_at_10_std
value: 13.791999999999998
- type: nauc_mrr_at_10_diff1
value: 20.7022
- type: nauc_mrr_at_20_max
value: 27.886499999999998
- type: nauc_mrr_at_20_std
value: 13.952700000000002
- type: nauc_mrr_at_20_diff1
value: 20.5573
- type: nauc_mrr_at_100_max
value: 27.714299999999998
- type: nauc_mrr_at_100_std
value: 13.863700000000001
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value: 20.5074
- type: nauc_mrr_at_1000_max
value: 27.700599999999998
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value: 13.8399
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value: 20.5031
- type: main_score
value: 24.146
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: pearson
value: 78.6926
- type: spearman
value: 71.2001
- type: cosine_pearson
value: 78.6926
- type: cosine_spearman
value: 71.2001
- type: manhattan_pearson
value: 75.264
- type: manhattan_spearman
value: 71.1303
- type: euclidean_pearson
value: 75.3261
- type: euclidean_spearman
value: 71.2001
- type: main_score
value: 71.2001
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: pearson
value: 71.0057
- type: spearman
value: 65.9247
- type: cosine_pearson
value: 71.0057
- type: cosine_spearman
value: 65.9247
- type: manhattan_pearson
value: 67.392
- type: manhattan_spearman
value: 65.8026
- type: euclidean_pearson
value: 67.5888
- type: euclidean_spearman
value: 65.92479999999999
- type: main_score
value: 65.9247
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: pearson
value: 81.67649999999999
- type: spearman
value: 81.7525
- type: cosine_pearson
value: 81.67649999999999
- type: cosine_spearman
value: 81.7525
- type: manhattan_pearson
value: 81.0327
- type: manhattan_spearman
value: 81.6717
- type: euclidean_pearson
value: 81.10000000000001
- type: euclidean_spearman
value: 81.7526
- type: main_score
value: 81.7525
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 79.47579999999999
- type: spearman
value: 74.2305
- type: cosine_pearson
value: 79.47579999999999
- type: cosine_spearman
value: 74.2305
- type: manhattan_pearson
value: 77.8846
- type: manhattan_spearman
value: 74.1908
- type: euclidean_pearson
value: 77.9333
- type: euclidean_spearman
value: 74.2305
- type: main_score
value: 74.2305
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 82.90180000000001
- type: spearman
value: 84.1271
- type: cosine_pearson
value: 82.90180000000001
- type: cosine_spearman
value: 84.1271
- type: manhattan_pearson
value: 83.6431
- type: manhattan_spearman
value: 84.1091
- type: euclidean_pearson
value: 83.6388
- type: euclidean_spearman
value: 84.127
- type: main_score
value: 84.1271
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 80.19810000000001
- type: spearman
value: 81.6627
- type: cosine_pearson
value: 80.19810000000001
- type: cosine_spearman
value: 81.6627
- type: manhattan_pearson
value: 81.4605
- type: manhattan_spearman
value: 81.62819999999999
- type: euclidean_pearson
value: 81.5043
- type: euclidean_spearman
value: 81.6627
- type: main_score
value: 81.6627
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 47.9276
- type: spearman
value: 50.0286
- type: cosine_pearson
value: 47.9276
- type: cosine_spearman
value: 50.0286
- type: manhattan_pearson
value: 48.5188
- type: manhattan_spearman
value: 50.432
- type: euclidean_pearson
value: 48.1655
- type: euclidean_spearman
value: 50.0286
- type: main_score
value: 50.0286
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 24.4119
- type: spearman
value: 22.1195
- type: cosine_pearson
value: 24.4119
- type: cosine_spearman
value: 22.1195
- type: manhattan_pearson
value: 25.873800000000003
- type: manhattan_spearman
value: 23.6049
- type: euclidean_pearson
value: 24.3693
- type: euclidean_spearman
value: 22.1195
- type: main_score
value: 22.1195
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 22.656200000000002
- type: spearman
value: 22.5445
- type: cosine_pearson
value: 22.656200000000002
- type: cosine_spearman
value: 22.5445
- type: manhattan_pearson
value: 22.414
- type: manhattan_spearman
value: 22.1601
- type: euclidean_pearson
value: 22.7736
- type: euclidean_spearman
value: 22.5445
- type: main_score
value: 22.5445
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 44.4998
- type: spearman
value: 43.1984
- type: cosine_pearson
value: 44.4998
- type: cosine_spearman
value: 43.1984
- type: manhattan_pearson
value: 43.3837
- type: manhattan_spearman
value: 43.1122
- type: euclidean_pearson
value: 44.1642
- type: euclidean_spearman
value: 43.1984
- type: main_score
value: 43.1984
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 82.3891
- type: spearman
value: 83.9634
- type: cosine_pearson
value: 82.3891
- type: cosine_spearman
value: 83.9634
- type: manhattan_pearson
value: 83.1481
- type: manhattan_spearman
value: 83.9743
- type: euclidean_pearson
value: 83.2767
- type: euclidean_spearman
value: 83.9634
- type: main_score
value: 83.9634
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 35.3106
- type: spearman
value: 30.7572
- type: cosine_pearson
value: 35.3106
- type: cosine_spearman
value: 30.7572
- type: manhattan_pearson
value: 35.6552
- type: manhattan_spearman
value: 31.596000000000004
- type: euclidean_pearson
value: 35.4393
- type: euclidean_spearman
value: 30.7572
- type: main_score
value: 30.7572
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 36.9322
- type: spearman
value: 37.7137
- type: cosine_pearson
value: 36.9322
- type: cosine_spearman
value: 37.7137
- type: manhattan_pearson
value: 36.0714
- type: manhattan_spearman
value: 36.9979
- type: euclidean_pearson
value: 36.784800000000004
- type: euclidean_spearman
value: 37.7137
- type: main_score
value: 37.7137
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 39.963300000000004
- type: spearman
value: 38.9248
- type: cosine_pearson
value: 39.963300000000004
- type: cosine_spearman
value: 38.9248
- type: manhattan_pearson
value: 39.539699999999996
- type: manhattan_spearman
value: 38.191900000000004
- type: euclidean_pearson
value: 39.8596
- type: euclidean_spearman
value: 38.9248
- type: main_score
value: 38.9248
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 56.0924
- type: spearman
value: 54.1844
- type: cosine_pearson
value: 56.0924
- type: cosine_spearman
value: 54.1844
- type: manhattan_pearson
value: 56.938100000000006
- type: manhattan_spearman
value: 53.9407
- type: euclidean_pearson
value: 57.9844
- type: euclidean_spearman
value: 54.1844
- type: main_score
value: 54.1844
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 69.3771
- type: spearman
value: 69.3609
- type: cosine_pearson
value: 69.3771
- type: cosine_spearman
value: 69.3609
- type: manhattan_pearson
value: 70.8762
- type: manhattan_spearman
value: 69.1889
- type: euclidean_pearson
value: 70.9433
- type: euclidean_spearman
value: 69.3609
- type: main_score
value: 69.3609
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 74.11609999999999
- type: spearman
value: 71.63340000000001
- type: cosine_pearson
value: 74.11609999999999
- type: cosine_spearman
value: 71.63340000000001
- type: manhattan_pearson
value: 73.2348
- type: manhattan_spearman
value: 71.1802
- type: euclidean_pearson
value: 73.284
- type: euclidean_spearman
value: 71.63340000000001
- type: main_score
value: 71.63340000000001
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 70.08879999999999
- type: spearman
value: 73.79
- type: cosine_pearson
value: 70.08879999999999
- type: cosine_spearman
value: 73.79
- type: manhattan_pearson
value: 71.5415
- type: manhattan_spearman
value: 73.6588
- type: euclidean_pearson
value: 71.621
- type: euclidean_spearman
value: 73.79
- type: main_score
value: 73.79
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 37.5935
- type: spearman
value: 39.5919
- type: cosine_pearson
value: 37.5935
- type: cosine_spearman
value: 39.5919
- type: manhattan_pearson
value: 37.1717
- type: manhattan_spearman
value: 38.6974
- type: euclidean_pearson
value: 37.5632
- type: euclidean_spearman
value: 39.5919
- type: main_score
value: 39.5919
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 79.9453
- type: spearman
value: 79.6569
- type: cosine_pearson
value: 79.9453
- type: cosine_spearman
value: 79.6569
- type: manhattan_pearson
value: 79.8923
- type: manhattan_spearman
value: 79.58370000000001
- type: euclidean_pearson
value: 79.9829
- type: euclidean_spearman
value: 79.6569
- type: main_score
value: 79.6569
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 88.09949999999999
- type: mrr
value: 96.6455
- type: nAUC_map_max
value: 53.3622
- type: nAUC_map_std
value: 70.3532
- type: nAUC_map_diff1
value: -0.21419999999999997
- type: nAUC_mrr_max
value: 88.893
- type: nAUC_mrr_std
value: 85.4516
- type: nAUC_mrr_diff1
value: 43.6847
- type: main_score
value: 88.09949999999999
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_3
value: 69.77600000000001
- type: ndcg_at_5
value: 71.964
- type: ndcg_at_10
value: 74.72
- type: ndcg_at_20
value: 76.154
- type: ndcg_at_100
value: 76.961
- type: ndcg_at_1000
value: 77.294
- type: map_at_1
value: 60.011
- type: map_at_3
value: 67.135
- type: map_at_5
value: 68.78
- type: map_at_10
value: 70.101
- type: map_at_20
value: 70.56099999999999
- type: map_at_100
value: 70.687
- type: map_at_1000
value: 70.699
- type: recall_at_1
value: 60.011
- type: recall_at_3
value: 74.839
- type: recall_at_5
value: 80.028
- type: recall_at_10
value: 87.8
- type: recall_at_20
value: 93.10000000000001
- type: recall_at_100
value: 97.333
- type: recall_at_1000
value: 100.0
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.8
- type: precision_at_10
value: 9.933
- type: precision_at_20
value: 5.283
- type: precision_at_100
value: 1.103
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 62.6667
- type: mrr_at_3
value: 68.9444
- type: mrr_at_5
value: 69.9611
- type: mrr_at_10
value: 71.02199999999999
- type: mrr_at_20
value: 71.3777
- type: mrr_at_100
value: 71.4841
- type: mrr_at_1000
value: 71.4961
- type: nauc_ndcg_at_1_max
value: 55.4562
- type: nauc_ndcg_at_1_std
value: -9.3317
- type: nauc_ndcg_at_1_diff1
value: 71.1878
- type: nauc_ndcg_at_3_max
value: 55.3473
- type: nauc_ndcg_at_3_std
value: -14.341400000000002
- type: nauc_ndcg_at_3_diff1
value: 69.11880000000001
- type: nauc_ndcg_at_5_max
value: 55.5531
- type: nauc_ndcg_at_5_std
value: -13.448699999999999
- type: nauc_ndcg_at_5_diff1
value: 67.4611
- type: nauc_ndcg_at_10_max
value: 59.5974
- type: nauc_ndcg_at_10_std
value: -10.262
- type: nauc_ndcg_at_10_diff1
value: 68.3408
- type: nauc_ndcg_at_20_max
value: 58.586499999999994
- type: nauc_ndcg_at_20_std
value: -9.8438
- type: nauc_ndcg_at_20_diff1
value: 68.4434
- type: nauc_ndcg_at_100_max
value: 58.28489999999999
- type: nauc_ndcg_at_100_std
value: -8.7782
- type: nauc_ndcg_at_100_diff1
value: 68.585
- type: nauc_ndcg_at_1000_max
value: 58.0138
- type: nauc_ndcg_at_1000_std
value: -9.4827
- type: nauc_ndcg_at_1000_diff1
value: 69.0467
- type: nauc_map_at_1_max
value: 49.434
- type: nauc_map_at_1_std
value: -17.0503
- type: nauc_map_at_1_diff1
value: 71.80290000000001
- type: nauc_map_at_3_max
value: 52.8035
- type: nauc_map_at_3_std
value: -16.2138
- type: nauc_map_at_3_diff1
value: 69.81739999999999
- type: nauc_map_at_5_max
value: 54.644400000000005
- type: nauc_map_at_5_std
value: -13.910900000000002
- type: nauc_map_at_5_diff1
value: 68.8879
- type: nauc_map_at_10_max
value: 56.550999999999995
- type: nauc_map_at_10_std
value: -12.126900000000001
- type: nauc_map_at_10_diff1
value: 69.2326
- type: nauc_map_at_20_max
value: 56.299699999999994
- type: nauc_map_at_20_std
value: -11.8978
- type: nauc_map_at_20_diff1
value: 69.3387
- type: nauc_map_at_100_max
value: 56.295300000000005
- type: nauc_map_at_100_std
value: -11.6546
- type: nauc_map_at_100_diff1
value: 69.3881
- type: nauc_map_at_1000_max
value: 56.2905
- type: nauc_map_at_1000_std
value: -11.666400000000001
- type: nauc_map_at_1000_diff1
value: 69.4106
- type: nauc_recall_at_1_max
value: 49.434
- type: nauc_recall_at_1_std
value: -17.0503
- type: nauc_recall_at_1_diff1
value: 71.80290000000001
- type: nauc_recall_at_3_max
value: 53.6504
- type: nauc_recall_at_3_std
value: -20.3796
- type: nauc_recall_at_3_diff1
value: 66.0397
- type: nauc_recall_at_5_max
value: 54.45140000000001
- type: nauc_recall_at_5_std
value: -17.8965
- type: nauc_recall_at_5_diff1
value: 60.6996
- type: nauc_recall_at_10_max
value: 72.7183
- type: nauc_recall_at_10_std
value: -7.3393
- type: nauc_recall_at_10_diff1
value: 62.0422
- type: nauc_recall_at_20_max
value: 70.7849
- type: nauc_recall_at_20_std
value: -3.1933000000000002
- type: nauc_recall_at_20_diff1
value: 58.146
- type: nauc_recall_at_100_max
value: 75.43769999999999
- type: nauc_recall_at_100_std
value: 36.5488
- type: nauc_recall_at_100_diff1
value: 46.3177
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_precision_at_1_max
value: 55.4562
- type: nauc_precision_at_1_std
value: -9.3317
- type: nauc_precision_at_1_diff1
value: 71.1878
- type: nauc_precision_at_3_max
value: 52.548300000000005
- type: nauc_precision_at_3_std
value: 6.719899999999999
- type: nauc_precision_at_3_diff1
value: 42.6315
- type: nauc_precision_at_5_max
value: 47.9921
- type: nauc_precision_at_5_std
value: 21.9242
- type: nauc_precision_at_5_diff1
value: 23.0825
- type: nauc_precision_at_10_max
value: 47.517399999999995
- type: nauc_precision_at_10_std
value: 44.4913
- type: nauc_precision_at_10_diff1
value: 5.4589
- type: nauc_precision_at_20_max
value: 36.0675
- type: nauc_precision_at_20_std
value: 53.9269
- type: nauc_precision_at_20_diff1
value: -7.0865
- type: nauc_precision_at_100_max
value: 28.0561
- type: nauc_precision_at_100_std
value: 66.17920000000001
- type: nauc_precision_at_100_diff1
value: -19.653000000000002
- type: nauc_precision_at_1000_max
value: 22.470100000000002
- type: nauc_precision_at_1000_std
value: 69.6725
- type: nauc_precision_at_1000_diff1
value: -27.430500000000002
- type: nauc_mrr_at_1_max
value: 55.4562
- type: nauc_mrr_at_1_std
value: -9.3317
- type: nauc_mrr_at_1_diff1
value: 71.1878
- type: nauc_mrr_at_3_max
value: 57.4634
- type: nauc_mrr_at_3_std
value: -10.6496
- type: nauc_mrr_at_3_diff1
value: 69.881
- type: nauc_mrr_at_5_max
value: 56.8667
- type: nauc_mrr_at_5_std
value: -10.2421
- type: nauc_mrr_at_5_diff1
value: 69.0777
- type: nauc_mrr_at_10_max
value: 58.06289999999999
- type: nauc_mrr_at_10_std
value: -9.8724
- type: nauc_mrr_at_10_diff1
value: 69.5505
- type: nauc_mrr_at_20_max
value: 57.740700000000004
- type: nauc_mrr_at_20_std
value: -10.0261
- type: nauc_mrr_at_20_diff1
value: 69.5455
- type: nauc_mrr_at_100_max
value: 57.735499999999995
- type: nauc_mrr_at_100_std
value: -9.8413
- type: nauc_mrr_at_100_diff1
value: 69.5846
- type: nauc_mrr_at_1000_max
value: 57.7313
- type: nauc_mrr_at_1000_std
value: -9.8523
- type: nauc_mrr_at_1000_diff1
value: 69.6076
- type: main_score
value: 74.72
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.798
- type: similarity_accuracy_threshold
value: 92.7546
- type: similarity_f1
value: 89.441
- type: similarity_f1_threshold
value: 92.7546
- type: similarity_precision
value: 92.70389999999999
- type: similarity_recall
value: 86.4
- type: similarity_ap
value: 95.40729999999999
- type: cosine_accuracy
value: 99.798
- type: cosine_accuracy_threshold
value: 92.7546
- type: cosine_f1
value: 89.441
- type: cosine_f1_threshold
value: 92.7546
- type: cosine_precision
value: 92.70389999999999
- type: cosine_recall
value: 86.4
- type: cosine_ap
value: 95.40729999999999
- type: manhattan_accuracy
value: 99.795
- type: manhattan_accuracy_threshold
value: 851.3785
- type: manhattan_f1
value: 89.5464
- type: manhattan_f1_threshold
value: 902.8005999999999
- type: manhattan_precision
value: 88.3268
- type: manhattan_recall
value: 90.8
- type: manhattan_ap
value: 95.3814
- type: euclidean_accuracy
value: 99.798
- type: euclidean_accuracy_threshold
value: 38.0669
- type: euclidean_f1
value: 89.441
- type: euclidean_f1_threshold
value: 38.0669
- type: euclidean_precision
value: 92.70389999999999
- type: euclidean_recall
value: 86.4
- type: euclidean_ap
value: 95.4074
- type: dot_accuracy
value: 99.798
- type: dot_accuracy_threshold
value: 92.7546
- type: dot_f1
value: 89.441
- type: dot_f1_threshold
value: 92.7546
- type: dot_precision
value: 92.70389999999999
- type: dot_recall
value: 86.4
- type: dot_ap
value: 95.4074
- type: max_accuracy
value: 99.798
- type: max_f1
value: 89.5464
- type: max_precision
value: 92.70389999999999
- type: max_recall
value: 90.8
- type: max_ap
value: 95.4074
- type: main_score
value: 95.4074
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 70.3156
- type: v_measure_std
value: 3.9677
- type: main_score
value: 70.3156
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 35.4198
- type: v_measure_std
value: 1.5537
- type: main_score
value: 35.4198
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.522099999999995
- type: mrr
value: 55.500099999999996
- type: nAUC_map_max
value: 7.9342
- type: nAUC_map_std
value: 6.8542000000000005
- type: nAUC_map_diff1
value: 38.738099999999996
- type: nAUC_mrr_max
value: 8.862
- type: nAUC_mrr_std
value: 7.2187
- type: nAUC_mrr_diff1
value: 38.5236
- type: main_score
value: 54.522099999999995
task:
type: Reranking
- dataset:
config: default
name: MTEB StackOverflowQA (default)
revision: db8f169f3894c14a00251061f957b2063eef2bd5
split: test
type: CoIR-Retrieval/stackoverflow-qa
metrics:
- type: ndcg_at_1
value: 83.2
- type: ndcg_at_3
value: 88.397
- type: ndcg_at_5
value: 89.202
- type: ndcg_at_10
value: 89.846
- type: ndcg_at_20
value: 90.235
- type: ndcg_at_100
value: 90.55199999999999
- type: ndcg_at_1000
value: 90.654
- type: map_at_1
value: 83.2
- type: map_at_3
value: 87.17
- type: map_at_5
value: 87.616
- type: map_at_10
value: 87.889
- type: map_at_20
value: 87.994
- type: map_at_100
value: 88.041
- type: map_at_1000
value: 88.045
- type: recall_at_1
value: 83.2
- type: recall_at_3
value: 91.926
- type: recall_at_5
value: 93.882
- type: recall_at_10
value: 95.838
- type: recall_at_20
value: 97.392
- type: recall_at_100
value: 99.047
- type: recall_at_1000
value: 99.85000000000001
- type: precision_at_1
value: 83.2
- type: precision_at_3
value: 30.642000000000003
- type: precision_at_5
value: 18.776
- type: precision_at_10
value: 9.584
- type: precision_at_20
value: 4.87
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 83.19959999999999
- type: mrr_at_3
value: 87.1698
- type: mrr_at_5
value: 87.6162
- type: mrr_at_10
value: 87.8891
- type: mrr_at_20
value: 87.99369999999999
- type: mrr_at_100
value: 88.0412
- type: mrr_at_1000
value: 88.045
- type: nauc_ndcg_at_1_max
value: 78.6007
- type: nauc_ndcg_at_1_std
value: -0.0095
- type: nauc_ndcg_at_1_diff1
value: 88.7762
- type: nauc_ndcg_at_3_max
value: 81.4239
- type: nauc_ndcg_at_3_std
value: 1.4683
- type: nauc_ndcg_at_3_diff1
value: 86.54220000000001
- type: nauc_ndcg_at_5_max
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value: 33.4423
- type: nauc_recall_at_1000_std
value: 60.764399999999995
- type: nauc_recall_at_1000_diff1
value: -32.4319
- type: nauc_precision_at_1_max
value: 55.0789
- type: nauc_precision_at_1_std
value: 42.7355
- type: nauc_precision_at_1_diff1
value: 21.276500000000002
- type: nauc_precision_at_3_max
value: 57.5971
- type: nauc_precision_at_3_std
value: 54.4791
- type: nauc_precision_at_3_diff1
value: -1.1622000000000001
- type: nauc_precision_at_5_max
value: 66.64750000000001
- type: nauc_precision_at_5_std
value: 57.5585
- type: nauc_precision_at_5_diff1
value: 2.9311
- type: nauc_precision_at_10_max
value: 58.767100000000006
- type: nauc_precision_at_10_std
value: 63.5528
- type: nauc_precision_at_10_diff1
value: -1.193
- type: nauc_precision_at_20_max
value: 47.964
- type: nauc_precision_at_20_std
value: 65.3738
- type: nauc_precision_at_20_diff1
value: -17.0707
- type: nauc_precision_at_100_max
value: 38.9039
- type: nauc_precision_at_100_std
value: 68.9848
- type: nauc_precision_at_100_diff1
value: -31.816699999999997
- type: nauc_precision_at_1000_max
value: 24.090700000000002
- type: nauc_precision_at_1000_std
value: 36.3251
- type: nauc_precision_at_1000_diff1
value: -30.1565
- type: nauc_mrr_at_1_max
value: 55.0789
- type: nauc_mrr_at_1_std
value: 42.7355
- type: nauc_mrr_at_1_diff1
value: 21.276500000000002
- type: nauc_mrr_at_3_max
value: 57.0157
- type: nauc_mrr_at_3_std
value: 44.9613
- type: nauc_mrr_at_3_diff1
value: 18.5485
- type: nauc_mrr_at_5_max
value: 57.0157
- type: nauc_mrr_at_5_std
value: 44.9613
- type: nauc_mrr_at_5_diff1
value: 18.5485
- type: nauc_mrr_at_10_max
value: 57.0157
- type: nauc_mrr_at_10_std
value: 44.9613
- type: nauc_mrr_at_10_diff1
value: 18.5485
- type: nauc_mrr_at_20_max
value: 57.0157
- type: nauc_mrr_at_20_std
value: 44.9613
- type: nauc_mrr_at_20_diff1
value: 18.5485
- type: nauc_mrr_at_100_max
value: 57.0157
- type: nauc_mrr_at_100_std
value: 44.9613
- type: nauc_mrr_at_100_diff1
value: 18.5485
- type: nauc_mrr_at_1000_max
value: 57.0157
- type: nauc_mrr_at_1000_std
value: 44.9613
- type: nauc_mrr_at_1000_diff1
value: 18.5485
- type: main_score
value: 69.259
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: ndcg_at_1
value: 23.469
- type: ndcg_at_3
value: 22.555
- type: ndcg_at_5
value: 20.97
- type: ndcg_at_10
value: 20.147000000000002
- type: ndcg_at_20
value: 22.56
- type: ndcg_at_100
value: 32.79
- type: ndcg_at_1000
value: 45.324
- type: map_at_1
value: 2.152
- type: map_at_3
value: 4.103
- type: map_at_5
value: 5.482
- type: map_at_10
value: 7.747
- type: map_at_20
value: 10.309
- type: map_at_100
value: 13.639999999999999
- type: map_at_1000
value: 15.235000000000001
- type: recall_at_1
value: 2.152
- type: recall_at_3
value: 5.531
- type: recall_at_5
value: 8.029
- type: recall_at_10
value: 13.331000000000001
- type: recall_at_20
value: 22.195
- type: recall_at_100
value: 45.35
- type: recall_at_1000
value: 83.447
- type: precision_at_1
value: 26.531
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 21.633
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_20
value: 15.408
- type: precision_at_100
value: 7.081999999999999
- type: precision_at_1000
value: 1.547
- type: mrr_at_1
value: 26.5306
- type: mrr_at_3
value: 38.7755
- type: mrr_at_5
value: 40.6122
- type: mrr_at_10
value: 41.3994
- type: mrr_at_20
value: 42.7601
- type: mrr_at_100
value: 43.0467
- type: mrr_at_1000
value: 43.0467
- type: nauc_ndcg_at_1_max
value: -19.1831
- type: nauc_ndcg_at_1_std
value: -13.1044
- type: nauc_ndcg_at_1_diff1
value: -8.6701
- type: nauc_ndcg_at_3_max
value: -31.2521
- type: nauc_ndcg_at_3_std
value: -9.1974
- type: nauc_ndcg_at_3_diff1
value: -17.0766
- type: nauc_ndcg_at_5_max
value: -29.9171
- type: nauc_ndcg_at_5_std
value: -2.2094
- type: nauc_ndcg_at_5_diff1
value: -10.8668
- type: nauc_ndcg_at_10_max
value: -24.5148
- type: nauc_ndcg_at_10_std
value: -0.45909999999999995
- type: nauc_ndcg_at_10_diff1
value: -10.705
- type: nauc_ndcg_at_20_max
value: -29.542
- type: nauc_ndcg_at_20_std
value: -0.1119
- type: nauc_ndcg_at_20_diff1
value: -6.4151
- type: nauc_ndcg_at_100_max
value: -27.276
- type: nauc_ndcg_at_100_std
value: 33.380900000000004
- type: nauc_ndcg_at_100_diff1
value: -1.097
- type: nauc_ndcg_at_1000_max
value: -28.0856
- type: nauc_ndcg_at_1000_std
value: 40.368700000000004
- type: nauc_ndcg_at_1000_diff1
value: -9.5892
- type: nauc_map_at_1_max
value: -17.891099999999998
- type: nauc_map_at_1_std
value: -20.8139
- type: nauc_map_at_1_diff1
value: 2.1289
- type: nauc_map_at_3_max
value: -18.5984
- type: nauc_map_at_3_std
value: -16.0226
- type: nauc_map_at_3_diff1
value: -0.681
- type: nauc_map_at_5_max
value: -9.8672
- type: nauc_map_at_5_std
value: -11.448
- type: nauc_map_at_5_diff1
value: 4.1101
- type: nauc_map_at_10_max
value: -5.8905
- type: nauc_map_at_10_std
value: -7.7416
- type: nauc_map_at_10_diff1
value: 2.0848999999999998
- type: nauc_map_at_20_max
value: -13.9206
- type: nauc_map_at_20_std
value: -4.9227
- type: nauc_map_at_20_diff1
value: 1.6968
- type: nauc_map_at_100_max
value: -15.116
- type: nauc_map_at_100_std
value: 10.9804
- type: nauc_map_at_100_diff1
value: 1.5921999999999998
- type: nauc_map_at_1000_max
value: -15.309000000000001
- type: nauc_map_at_1000_std
value: 15.207399999999998
- type: nauc_map_at_1000_diff1
value: 0.2635
- type: nauc_recall_at_1_max
value: -17.891099999999998
- type: nauc_recall_at_1_std
value: -20.8139
- type: nauc_recall_at_1_diff1
value: 2.1289
- type: nauc_recall_at_3_max
value: -27.4434
- type: nauc_recall_at_3_std
value: -14.4615
- type: nauc_recall_at_3_diff1
value: -4.6056
- type: nauc_recall_at_5_max
value: -17.3993
- type: nauc_recall_at_5_std
value: -7.1856
- type: nauc_recall_at_5_diff1
value: 2.468
- type: nauc_recall_at_10_max
value: -13.7175
- type: nauc_recall_at_10_std
value: -2.9436
- type: nauc_recall_at_10_diff1
value: 0.9384
- type: nauc_recall_at_20_max
value: -26.96
- type: nauc_recall_at_20_std
value: -1.6922
- type: nauc_recall_at_20_diff1
value: 1.8932999999999998
- type: nauc_recall_at_100_max
value: -23.5556
- type: nauc_recall_at_100_std
value: 48.9062
- type: nauc_recall_at_100_diff1
value: 7.8596
- type: nauc_recall_at_1000_max
value: -19.6066
- type: nauc_recall_at_1000_std
value: 80.4306
- type: nauc_recall_at_1000_diff1
value: -8.4789
- type: nauc_precision_at_1_max
value: -23.163800000000002
- type: nauc_precision_at_1_std
value: -15.9221
- type: nauc_precision_at_1_diff1
value: -1.0075
- type: nauc_precision_at_3_max
value: -34.2
- type: nauc_precision_at_3_std
value: -5.8114
- type: nauc_precision_at_3_diff1
value: -11.4192
- type: nauc_precision_at_5_max
value: -28.3543
- type: nauc_precision_at_5_std
value: 3.2409
- type: nauc_precision_at_5_diff1
value: -2.4743
- type: nauc_precision_at_10_max
value: -21.8691
- type: nauc_precision_at_10_std
value: 12.0827
- type: nauc_precision_at_10_diff1
value: -7.6671000000000005
- type: nauc_precision_at_20_max
value: -29.541600000000003
- type: nauc_precision_at_20_std
value: 18.4544
- type: nauc_precision_at_20_diff1
value: -4.9384
- type: nauc_precision_at_100_max
value: -13.991700000000002
- type: nauc_precision_at_100_std
value: 80.9784
- type: nauc_precision_at_100_diff1
value: 0.1001
- type: nauc_precision_at_1000_max
value: 18.334
- type: nauc_precision_at_1000_std
value: 35.3463
- type: nauc_precision_at_1000_diff1
value: -16.8628
- type: nauc_mrr_at_1_max
value: -23.163800000000002
- type: nauc_mrr_at_1_std
value: -15.9221
- type: nauc_mrr_at_1_diff1
value: -1.0075
- type: nauc_mrr_at_3_max
value: -37.628099999999996
- type: nauc_mrr_at_3_std
value: -13.678199999999999
- type: nauc_mrr_at_3_diff1
value: -8.0387
- type: nauc_mrr_at_5_max
value: -38.205
- type: nauc_mrr_at_5_std
value: -10.0574
- type: nauc_mrr_at_5_diff1
value: -7.273300000000001
- type: nauc_mrr_at_10_max
value: -38.2773
- type: nauc_mrr_at_10_std
value: -10.5208
- type: nauc_mrr_at_10_diff1
value: -7.556400000000001
- type: nauc_mrr_at_20_max
value: -38.8068
- type: nauc_mrr_at_20_std
value: -10.7195
- type: nauc_mrr_at_20_diff1
value: -6.7631
- type: nauc_mrr_at_100_max
value: -38.318200000000004
- type: nauc_mrr_at_100_std
value: -10.854999999999999
- type: nauc_mrr_at_100_diff1
value: -6.843000000000001
- type: nauc_mrr_at_1000_max
value: -38.318200000000004
- type: nauc_mrr_at_1000_std
value: -10.854999999999999
- type: nauc_mrr_at_1000_diff1
value: -6.843000000000001
- type: main_score
value: 20.147000000000002
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 59.7607
- type: f1
value: 45.7266
- type: f1_weighted
value: 68.3382
- type: ap
value: 9.8682
- type: ap_weighted
value: 9.8682
- type: main_score
value: 59.7607
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 53.3192
- type: f1
value: 53.505100000000006
- type: f1_weighted
value: 52.726600000000005
- type: main_score
value: 53.3192
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 48.3133
- type: v_measure_std
value: 1.6674000000000002
- type: main_score
value: 48.3133
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: similarity_accuracy
value: 82.2972
- type: similarity_accuracy_threshold
value: 92.5986
- type: similarity_f1
value: 58.2994
- type: similarity_f1_threshold
value: 89.689
- type: similarity_precision
value: 53.3772
- type: similarity_recall
value: 64.2216
- type: similarity_ap
value: 60.9374
- type: cosine_accuracy
value: 82.2972
- type: cosine_accuracy_threshold
value: 92.5986
- type: cosine_f1
value: 58.2994
- type: cosine_f1_threshold
value: 89.689
- type: cosine_precision
value: 53.3772
- type: cosine_recall
value: 64.2216
- type: cosine_ap
value: 60.9374
- type: manhattan_accuracy
value: 82.2912
- type: manhattan_accuracy_threshold
value: 839.1809000000001
- type: manhattan_f1
value: 58.2447
- type: manhattan_f1_threshold
value: 996.9049
- type: manhattan_precision
value: 53.74830000000001
- type: manhattan_recall
value: 63.562
- type: manhattan_ap
value: 60.8808
- type: euclidean_accuracy
value: 82.2972
- type: euclidean_accuracy_threshold
value: 38.4743
- type: euclidean_f1
value: 58.2994
- type: euclidean_f1_threshold
value: 45.4114
- type: euclidean_precision
value: 53.3772
- type: euclidean_recall
value: 64.2216
- type: euclidean_ap
value: 60.9374
- type: dot_accuracy
value: 82.2972
- type: dot_accuracy_threshold
value: 92.5986
- type: dot_f1
value: 58.2994
- type: dot_f1_threshold
value: 89.689
- type: dot_precision
value: 53.3772
- type: dot_recall
value: 64.2216
- type: dot_ap
value: 60.9374
- type: max_accuracy
value: 82.2972
- type: max_f1
value: 58.2994
- type: max_precision
value: 53.74830000000001
- type: max_recall
value: 64.2216
- type: max_ap
value: 60.9374
- type: main_score
value: 60.9374
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 87.2162
- type: similarity_accuracy_threshold
value: 91.6164
- type: similarity_f1
value: 74.8086
- type: similarity_f1_threshold
value: 90.18260000000001
- type: similarity_precision
value: 69.3065
- type: similarity_recall
value: 81.25959999999999
- type: similarity_ap
value: 82.53160000000001
- type: cosine_accuracy
value: 87.2162
- type: cosine_accuracy_threshold
value: 91.6164
- type: cosine_f1
value: 74.8086
- type: cosine_f1_threshold
value: 90.18260000000001
- type: cosine_precision
value: 69.3065
- type: cosine_recall
value: 81.25959999999999
- type: cosine_ap
value: 82.53160000000001
- type: manhattan_accuracy
value: 87.21039999999999
- type: manhattan_accuracy_threshold
value: 899.2865999999999
- type: manhattan_f1
value: 74.77510000000001
- type: manhattan_f1_threshold
value: 962.114
- type: manhattan_precision
value: 70.6927
- type: manhattan_recall
value: 79.3579
- type: manhattan_ap
value: 82.5262
- type: euclidean_accuracy
value: 87.2162
- type: euclidean_accuracy_threshold
value: 40.9478
- type: euclidean_f1
value: 74.8086
- type: euclidean_f1_threshold
value: 44.3112
- type: euclidean_precision
value: 69.3065
- type: euclidean_recall
value: 81.25959999999999
- type: euclidean_ap
value: 82.53160000000001
- type: dot_accuracy
value: 87.2162
- type: dot_accuracy_threshold
value: 91.6164
- type: dot_f1
value: 74.8086
- type: dot_f1_threshold
value: 90.18260000000001
- type: dot_precision
value: 69.3065
- type: dot_recall
value: 81.25959999999999
- type: dot_ap
value: 82.53160000000001
- type: max_accuracy
value: 87.2162
- type: max_f1
value: 74.8086
- type: max_precision
value: 70.6927
- type: max_recall
value: 81.25959999999999
- type: max_ap
value: 82.53160000000001
- type: main_score
value: 82.53160000000001
task:
type: PairClassification
pipeline_tag: sentence-similarity
---
# Granite-Embedding-125m-English
**News:**
Granite Embedding R2 models with 8192 context length released.
- [granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-english-r2) (149M parameters): with an output embedding size of 768, replacing granite-embedding-125m-english.
- [granite-embedding-small-english-r2](https://huggingface.co/ibm-granite/granite-embedding-small-english-r2) (47M parameters): A first-of-its-kind reduced-size model, with fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english.
**Model Summary:**
Granite-Embedding-125m-English is a 125M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation.
- **Developers:** Granite Embedding Team, IBM
- **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Technical Report](https://arxiv.org/abs/2502.20204)
- **Release Date**: December 18th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Languages:**
English.
**Intended use:**
The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
**Usage with Sentence Transformers:**
The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
```shell
pip install sentence_transformers
```
The model can then be used to encode pairs of text and find the similarity between their representations
```python
from sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-125m-english"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
input_passages = [
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
```
**Usage with Huggingface Transformers:**
This is a simple example of how to use the Granite-Embedding-125m-English model with the Transformers library and PyTorch.
First, install the required libraries
```shell
pip install transformers torch
```
The model can then be used to encode pairs of text
```python
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-125m-english"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode queries
with torch.no_grad():
# Queries
model_output = model(**tokenized_queries)
# Perform pooling. granite-embedding-125m-english uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
The performance of the Granite-Embedding-125M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below.
| Model | Paramters (M)| Embedding Dimension | MTEB Retrieval (15) | CoIR (10) |
|---------------------------------|:------------:|:-------------------:|:-------------------: |:----------:|
|granite-embedding-125m-english |125 |768 |52.3 |50.3 |
**Model Architecture:**
Granite-Embedding-125m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
| :--------- | :-------:| :--------: | :-----:| :-----:|
| Embedding size | 384 | **768** | 384 | 768 |
| Number of layers | 6 | **12** | 6 | 12 |
| Number of attention heads | 12 | **12** | 12 | 12 |
| Intermediate size | 1536 | **3072** | 1536 | 3072 |
| Activation Function | GeLU | **GeLU** | GeLU | GeLU |
| Vocabulary Size | 50265| **50265** | 250002 | 250002 |
| Max. Sequence Length | 512 | **512** | 512 | 512 |
| # Parameters | 30M | **125M** | 107M | 278M |
**Training Data:**
Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
| **Dataset** | **Num. Pairs** |
|----------------------------------------------------|:---------------:|
| SPECTER citation triplets | 684,100 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Stack Exchange Duplicate questions (titles+bodies) | 250,460 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| PAQ (Question, Answer) pairs | 64,371,441 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title+Body, Answer) pairs | 187,195 |
| S2ORC Citation pairs (Titles) | 52,603,982 |
| S2ORC (Title, Abstract) | 41,769,185 |
| S2ORC (Citations, abstracts) | 52,603,982 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| SearchQA | 582,261 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| Arxiv | 2,358,545 |
| Wikipedia | 20,745,403 |
| PubMed | 20,000,000 |
| Miracl En Pairs | 9,016 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: Query-Wikipedia Passage | 1,879,093 |
| Synthetic: Fact Verification | 9,888 |
| IBM Internal Triples | 40,290 |
| IBM Internal Title-Body Pairs | 1,524,586 |
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
**Infrastructure:**
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
**Ethical Considerations and Limitations:**
The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-125m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
**Resources**
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
<!-- ## Citation
```
@misc{granite-embedding-models,
author = {author 1, author2, ...},
title = {},
journal = {},
volume = {},
year = {2024},
url = {https://arxiv.org/abs/0000.00000},
}
``` -->
|
g-assismoraes/Qwen3-4B-Base-aki-alpha0.08-var-hatebr-ep30-g5-v3
|
g-assismoraes
| 2025-08-19T01:28:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T01:24:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### 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. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
teapotai/teapotllm
|
teapotai
| 2025-08-19T01:26:28Z | 61 | 179 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"t5",
"text2text-generation",
"text-generation",
"transformers.js",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:teapotai/synthqa",
"dataset:teapotai/teapot-chat",
"base_model:google/flan-t5-large",
"base_model:quantized:google/flan-t5-large",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-19T02:29:11Z |
---
license: mit
datasets:
- teapotai/synthqa
- teapotai/teapot-chat
language:
- en
- fr
- ro
- de
- multilingual
library_name: transformers
tags:
- text-generation
- transformers.js
widget:
- text: >-
Teapot is an open-source small language model (~800 million parameters)
fine-tuned on synthetic data and optimized to run locally on
resource-constrained devices such as smartphones and CPUs. Teapot is trained
to only answer using context from documents, reducing hallucinations. Teapot
can perform a variety of tasks, including hallucination-resistant Question
Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
by and for the community.
What devices can teapot run on?
example_title: Question Answering
- text: >-
Teapot is an open-source small language model (~800 million parameters)
fine-tuned on synthetic data and optimized to run locally on
resource-constrained devices such as smartphones and CPUs. Teapot is trained
to only answer using context from documents, reducing hallucinations. Teapot
can perform a variety of tasks, including hallucination-resistant Question
Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
by and for the community.
Tell me about teapotllm
example_title: Summarization Answering
- text: >-
Teapot is an open-source small language model (~800 million parameters)
fine-tuned on synthetic data and optimized to run locally on
resource-constrained devices such as smartphones and CPUs. Teapot is trained
to only answer using context from documents, reducing hallucinations. Teapot
can perform a variety of tasks, including hallucination-resistant Question
Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
by and for the community.
Extract the number of parameters
example_title: Information Extraction
- text: >-
Teapot is an open-source small language model (~800 million parameters)
fine-tuned on synthetic data and optimized to run locally on
resource-constrained devices such as smartphones and CPUs. Teapot is trained
to only answer using context from documents, reducing hallucinations. Teapot
can perform a variety of tasks, including hallucination-resistant Question
Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction.
TeapotLLM is a fine tune of flan-t5-large that was trained on synthetic data
generated by Deepseek v3 TeapotLLM can be hosted on low-power devices with
as little as 2GB of CPU RAM such as a Raspberry Pi. Teapot is a model built
by and for the community.
How many parameters is Deepseek?
example_title: Hallucination Resistance
base_model:
- google/flan-t5-large
pipeline_tag: text2text-generation
---
# Teapot LLM
[Website](https://teapotai.com/) | [Try out our demo on Discord](https://discord.gg/hPxGSn5dST)
Teapot is an open-source small language model (~800 million parameters) fine-tuned on synthetic data and optimized to run locally on resource-constrained devices such as smartphones and CPUs. Teapot is trained to only answer using context from documents, reducing hallucinations. Teapot can perform a variety of tasks, including hallucination-resistant Question Answering (QnA), Retrieval-Augmented Generation (RAG), and JSON extraction. Teapot is a model built by and for the community.
[Evaluation Details](https://huggingface.co/teapotai/teapotllm#model-evaluation)
### Conversational Question Answering
Teapot is fine-tuned to provide friendly, conversational answers using context and documents provided as references.
### Hallucination Resistance
Teapot is trained to only output answers that can be derived from the provided context, ensuring that even though it is a small model, it performs demonstrably better by refusing to answer questions when there is insufficient data.
### Retrieval Augmented Generation
Teapot is further fine-tuned on the task of retrieval augmented generation by utilizing a custom [embedding model](https://huggingface.co/teapotai/teapotembedding). We perform RAG across multiple documents from our training data and the model is able to learn to extract relevant details for question answering.
### Information Extraction
Teapot has been trained to extract succint answers in a variety of format enabling efficient document parsing. Teapot is trained natively to output standard data types such as numbers, strings, and even json.
---
## Getting Started
We recommend using our library [teapotai](https://pypi.org/project/teapotai/) to quickly integrate our models into production environments, as it handles the overhead of model configuration, document embeddings, error handling and prompt formatting. However, you can directly use the model from the transformers library on huggingface.
### Installation
```bash
! pip install teapotai
```
---
### 1. General Question Answering (QnA)
Teapot can be used for general question answering based on a provided context. The model is optimized to respond conversationally and is trained to avoid answering questions that can't be answered from the given context, reducing hallucinations.
#### Example:
```python
from teapotai import TeapotAI
# Sample context
context = """
The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889.
It stands at a height of 330 meters and is one of the most recognizable structures in the world.
"""
teapot_ai = TeapotAI()
answer = teapot_ai.query(
query="What is the height of the Eiffel Tower?",
context=context
)
print(answer) # => "The Eiffel Tower stands at a height of 330 meters. "
```
#### Hallucination Example:
```python
from teapotai import TeapotAI
# Sample context without height information
context = """
The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889.
"""
teapot_ai = TeapotAI()
answer = teapot_ai.query(
query="What is the height of the Eiffel Tower?",
context=context
)
print(answer) # => "I don't have information on the height of the Eiffel Tower."
```
---
### 2. Chat with Retrieval Augmented Generation (RAG)
Teapot can also use Retrieval-Augmented Generation (RAG) to determine which documents are relevant before answering a question. This is useful when you have many documents you want to use as context, ensuring the model answers based on the most relevant ones.
#### Example:
```python
from teapotai import TeapotAI
# Sample documents (in practice, these could be articles or longer documents)
documents = [
"The Eiffel Tower is located in Paris, France. It was built in 1889 and stands 330 meters tall.",
"The Great Wall of China is a historic fortification that stretches over 13,000 miles.",
"The Amazon Rainforest is the largest tropical rainforest in the world, covering over 5.5 million square kilometers.",
"The Grand Canyon is a natural landmark located in Arizona, USA, carved by the Colorado River.",
"Mount Everest is the tallest mountain on Earth, located in the Himalayas along the border between Nepal and China.",
"The Colosseum in Rome, Italy, is an ancient amphitheater known for its gladiator battles.",
"The Sahara Desert is the largest hot desert in the world, located in North Africa.",
"The Nile River is the longest river in the world, flowing through northeastern Africa.",
"The Empire State Building is an iconic skyscraper in New York City that was completed in 1931 and stands at 1454 feet tall."
]
# Initialize TeapotAI with documents for RAG
teapot_ai = TeapotAI(documents=documents)
# Get the answer using RAG
answer = teapot_ai.chat([
{
"role":"system",
"content": "You are an agent designed to answer facts about famous landmarks."
},
{
"role":"user",
"content": "What landmark was constructed in the 1800s?"
}
])
print(answer) # => The Eiffel Tower was constructed in the 1800s.
```
#### Loading RAG Model:
You can save a model with pre-computed embeddings to reduce loading times. TeapotAI is pickle-compatible and can be saved and loaded as shown below.
```python
import pickle
# Pickle the TeapotAI model to a file with pre-computed embeddings
with open("teapot_ai.pkl", "wb") as f:
pickle.dump(teapot_ai, f)
# Load the pickled model
with open("teapot_ai.pkl", "rb") as f:
loaded_teapot_ai = pickle.load(f)
# You can now use the loaded instance as you would normally
print(len(loaded_teapot_ai.documents)) # => 10 Documents with precomputed embeddings
loaded_teapot_ai.query("What city is the Eiffel Tower in?") # => "The Eiffel Tower is located in Paris, France."
```
---
### 3. Information Extraction
Teapot can be used to extract structured information from context using pre-defined JSON structures. The extract method takes a Pydantic model to ensure Teapot extracts the correct types. Teapot can infer fields based on names and will also leverage descriptions if available. This method can also be used with RAG and query functionalities natively.
#### Example:
```python
from teapotai import TeapotAI
from pydantic import BaseModel
# Sample text containing apartment details
apartment_description = """
This spacious 2-bedroom apartment is available for rent in downtown New York. The monthly rent is $2500.
It includes 1 bathrooms and a fully equipped kitchen with modern appliances.
Pets are welcome!
Please reach out to us at 555-123-4567 or john@realty.com
"""
# Define a Pydantic model for the data you want to extract
class ApartmentInfo(BaseModel):
rent: float = Field(..., description="the monthly rent in dollars")
bedrooms: int = Field(..., description="the number of bedrooms")
bathrooms: int = Field(..., description="the number of bathrooms")
phone_number: str
# Initialize TeapotAI
teapot_ai = TeapotAI()
# Extract the apartment details
extracted_info = teapot_ai.extract(
ApartmentInfo,
context=apartment_description
)
print(extracted_info) # => ApartmentInfo(rent=2500.0 bedrooms=2 bathrooms=1 phone_number='555-123-4567')
```
### Native Transformer Support
While we recommend using TeapotAI's library, you can load the base model directly with Hugging Face's Transformers library as follows:
```python
from transformers import pipeline
# Load the model
teapot_ai = pipeline("text2text-generation", "teapotai/teapotllm")
context = """
The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889.
It stands at a height of 330 meters and is one of the most recognizable structures in the world.
"""
question = "What is the height of the Eiffel Tower?"
answer = teapot_ai(context+"\n"+question)
print(answer[0].get('generated_text')) # => The Eiffel Tower stands at a height of 330 meters.
```
### Transformers.js Support
You can even run the model in-browser (or any other JavaScript environment) with [Transformers.js](https://huggingface.co/docs/transformers.js) as follows:
```js
// npm i @huggingface/transformers
import { pipeline } from "@huggingface/transformers";
const teapot_ai = await pipeline("text2text-generation", "teapotai/teapotllm");
const context = `
The Eiffel Tower is a wrought iron lattice tower in Paris, France. It was designed by Gustave Eiffel and completed in 1889.
It stands at a height of 330 meters and is one of the most recognizable structures in the world.
`;
const question = "What is the height of the Eiffel Tower?";
const answer = await teapot_ai(context + "\n" + question);
console.log(answer[0].generated_text); // => " The Eiffel Tower stands at a height of 330 meters."
```
---
## Model Details
Teapot LLM is fine-tuned from [flan-t5-large](https://huggingface.co/google/flan-t5-large) on a [synthetic dataset](https://huggingface.co/datasets/teapotai/synthqa) of LLM tasks generated using [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3).
### Training Details
- [Dataset] ~10mb synthetic dataset consisting of QnA pairs with a variety of task specific formats.
- [Methodology] The model is trained to mimic task specific output formats, and is scored based on its ability to output relevant, succint and verifiable answers in the requested format.
- [Hardware] Teapot was trained for ~10hr on an A100 provided by Google Colab.
- [Hyperparameters] The model was trained with various learning rates and monitored to ensure task specific performance was learned without catastrophic forgetting.
### Model Evaluation
TeapotLLM is focused on in-context reasoning tasks, and therefore most benchmarks are not suitable for evaluation. We want TeapotLLM to be a practical tool for QnA and information extraction, so we have developed custom datasets to benchmark performance.
[Evaluation Notebook Here](https://github.com/zakerytclarke/teapot/blob/main/docs/evals/TeapotLLM_Benchmark.ipynb)
#### Synthqa Evaluation
[Synthqa](https://huggingface.co/datasets/teapotai/synthqa) is a dataset focused on in-context QnA and information extraction tasks. We use the validation set to benchmark TeapotLLM against other models of similar size. All benchmarks were run using a Google Colab Notebook running on CPU with High Ram. Teapot significantly outperforms models of similar size, with low latency CPU inference and improved accuracy.
We also manually annotated hallucination refusals from models. All models were asked to not answer if the answer could not be derived from the provided context. TeapotLLM exhibits significantly stronger hallucination resistant behavior, without compromising on incorrect refusals.
### Limitations and Risks
Teapot is trained specifically for question answering use cases and is not intended to be used for code generation, creative writing or critical decision applications. Teapot has only been trained on specific languages supported by flan-t5 and has not been evaluated for performance in languages other than English.
### License
This model, the embedding model and the synthetic dataset are all provided open source under the MIT LICENSE.
## Questions, Feature Requests?
We hope you find TeapotAI useful and are continuosuly working to improve our models. Please reach out to us on our [Discord](https://discord.gg/hPxGSn5dST) for any technical help or feature requrests. We look forwarding to seeing what our community can build!
|
dgambettaphd/M_mis_run2_gen0_WXS_doc1000_synt64_lr1e-04_acm_MPP
|
dgambettaphd
| 2025-08-19T01:16:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-19T01:14:32Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755566034
|
IvanJAjebu
| 2025-08-19T01:15:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:15:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755563921
|
quantumxnode
| 2025-08-19T01:06:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:06:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755563907
|
helmutsukocok
| 2025-08-19T01:05:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:05:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abcorrea/p2-v6
|
abcorrea
| 2025-08-19T01:01:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:abcorrea/p2-v5",
"base_model:finetune:abcorrea/p2-v5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T00:49:18Z |
---
base_model: abcorrea/p2-v5
library_name: transformers
model_name: p2-v6
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for p2-v6
This model is a fine-tuned version of [abcorrea/p2-v5](https://huggingface.co/abcorrea/p2-v5).
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="abcorrea/p2-v6", 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: 0.19.1
- Transformers: 4.52.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.1
## 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}}
}
```
|
Cristhian2430/whisper-large-coes-v9
|
Cristhian2430
| 2025-08-19T00:58:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"es",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-18T14:58:39Z |
---
library_name: transformers
language:
- es
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large SEIN - COES SEIN - Version 9
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. -->
# Whisper Large SEIN - COES SEIN - Version 9
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the SEIN COES dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8246
- Wer: 67.8191
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.0123 | 90.9091 | 1000 | 3.1299 | 68.9362 |
| 0.0054 | 181.8182 | 2000 | 3.3876 | 69.0957 |
| 0.0002 | 272.7273 | 3000 | 3.5715 | 69.5213 |
| 0.0001 | 363.6364 | 4000 | 3.6546 | 68.3511 |
| 0.0002 | 454.5455 | 5000 | 3.7197 | 67.6596 |
| 0.0001 | 545.4545 | 6000 | 3.7702 | 67.5532 |
| 0.0001 | 636.3636 | 7000 | 3.8096 | 67.7660 |
| 0.0001 | 727.2727 | 8000 | 3.8246 | 67.8191 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
colabbear/bge-reranker-v2-m3-ko-bnb-4bit
|
colabbear
| 2025-08-19T00:54:32Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"bnb-my-repo",
"text-ranking",
"ko",
"en",
"base_model:dragonkue/bge-reranker-v2-m3-ko",
"base_model:quantized:dragonkue/bge-reranker-v2-m3-ko",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-ranking
| 2025-08-19T00:54:26Z |
---
base_model:
- dragonkue/bge-reranker-v2-m3-ko
license: apache-2.0
language:
- ko
- en
metrics:
- accuracy
pipeline_tag: text-ranking
library_name: sentence-transformers
tags:
- bnb-my-repo
---
# dragonkue/bge-reranker-v2-m3-ko (Quantized)
## Description
This model is a quantized version of the original model [`dragonkue/bge-reranker-v2-m3-ko`](https://huggingface.co/dragonkue/bge-reranker-v2-m3-ko).
It's quantized using the BitsAndBytes library to 4-bit using the [bnb-my-repo](https://huggingface.co/spaces/bnb-community/bnb-my-repo) space.
## Quantization Details
- **Quantization Type**: int4
- **bnb_4bit_quant_type**: nf4
- **bnb_4bit_use_double_quant**: True
- **bnb_4bit_compute_dtype**: bfloat16
- **bnb_4bit_quant_storage**: uint8
# 📄 Original Model Information
<img src="https://cdn-uploads.huggingface.co/production/uploads/642b0c2fecec03b4464a1d9b/IxcqY5qbGNuGpqDciIcOI.webp" width="600">
# Reranker (Cross-Encoder)
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
## Model Details
- Base model : BAAI/bge-reranker-v2-m3
- The multilingual model has been optimized for Korean.
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('dragonkue/bge-reranker-v2-m3-ko')
tokenizer = AutoTokenizer.from_pretrained('dragonkue/bge-reranker-v2-m3-ko')
features = tokenizer([['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'],
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.']], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
logits = model(**features).logits
scores = torch.sigmoid(logits)
print(scores)
# [9.9997962e-01 5.0702977e-07]
```
## Usage with SentenceTransformers
First install the Sentence Transformers library:
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('dragonkue/bge-reranker-v2-m3-ko', default_activation_function=torch.nn.Sigmoid())
scores = model.predict([['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'],
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.']])
print(scores)
# [9.9997962e-01 5.0702977e-07]
```
## Usage with FlagEmbedding
First install the FlagEmbedding library:
```
pip install -U FlagEmbedding
```
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('dragonkue/bge-reranker-v2-m3-ko')
scores = reranker.compute_score([['몇 년도에 지방세외수입법이 시행됐을까?', '실무교육을 통해 ‘지방세외수입법’에 대한 자치단체의 관심을 제고하고 자치단체의 차질 없는 업무 추진을 지원하였다. 이러한 준비과정을 거쳐 2014년 8월 7일부터 ‘지방세외수입법’이 시행되었다.'],
['몇 년도에 지방세외수입법이 시행됐을까?', '식품의약품안전처는 21일 국내 제약기업 유바이오로직스가 개발 중인 신종 코로나바이러스 감염증(코로나19) 백신 후보물질 ‘유코백-19’의 임상시험 계획을 지난 20일 승인했다고 밝혔다.']], normalize=True)
print(scores)
# [9.9997962e-01 5.0702977e-07]
```
## Fine-tune
Refer to https://github.com/FlagOpen/FlagEmbedding
## Evaluation
### Bi-encoder and Cross-encoder
Bi-Encoders convert texts into fixed-size vectors and efficiently calculate similarities between them. They are fast and ideal for tasks like semantic search and classification, making them suitable for processing large datasets quickly.
Cross-Encoders directly compare pairs of texts to compute similarity scores, providing more accurate results. While they are slower due to needing to process each pair, they excel in re-ranking top results and are important in Advanced RAG techniques for enhancing text generation.
### Korean Embedding Benchmark with AutoRAG
(https://github.com/Marker-Inc-Korea/AutoRAG-example-korean-embedding-benchmark)
This is a Korean embedding benchmark for the financial sector.
**Top-k 1**
Bi-Encoder (Sentence Transformer)
| Model name | F1 | Recall | Precision |
|---------------------------------------|------------|------------|------------|
| paraphrase-multilingual-mpnet-base-v2 | 0.3596 | 0.3596 | 0.3596 |
| KoSimCSE-roberta | 0.4298 | 0.4298 | 0.4298 |
| Cohere embed-multilingual-v3.0 | 0.3596 | 0.3596 | 0.3596 |
| openai ada 002 | 0.4737 | 0.4737 | 0.4737 |
| multilingual-e5-large-instruct | 0.4649 | 0.4649 | 0.4649 |
| Upstage Embedding | 0.6579 | 0.6579 | 0.6579 |
| paraphrase-multilingual-MiniLM-L12-v2 | 0.2982 | 0.2982 | 0.2982 |
| openai_embed_3_small | 0.5439 | 0.5439 | 0.5439 |
| ko-sroberta-multitask | 0.4211 | 0.4211 | 0.4211 |
| openai_embed_3_large | 0.6053 | 0.6053 | 0.6053 |
| KU-HIAI-ONTHEIT-large-v1 | 0.7105 | 0.7105 | 0.7105 |
| KU-HIAI-ONTHEIT-large-v1.1 | 0.7193 | 0.7193 | 0.7193 |
| kf-deberta-multitask | 0.4561 | 0.4561 | 0.4561 |
| gte-multilingual-base | 0.5877 | 0.5877 | 0.5877 |
| KoE5 | 0.7018 | 0.7018 | 0.7018 |
| BGE-m3 | 0.6578 | 0.6578 | 0.6578 |
| bge-m3-korean | 0.5351 | 0.5351 | 0.5351 |
| **BGE-m3-ko** | **0.7456** | **0.7456** | **0.7456** |
Cross-Encoder (Reranker)
| Model name | F1 | Recall | Precision |
|---------------------------------------|------------|------------|------------|
| gte-multilingual-reranker-base | 0.7281 | 0.7281 | 0.7281 |
| jina-reranker-v2-base-multilingual | 0.8070 | 0.8070 | 0.8070 |
| bge-reranker-v2-m3 | 0.8772 | 0.8772 | 0.8772 |
| upskyy/ko-reranker-8k | 0.8684| 0.8684 | 0.8684 |
| upskyy/ko-reranker | 0.8333| 0.8333 | 0.8333 |
| mncai/bge-ko-reranker-560M | 0.0088| 0.0088 | 0.0088 |
| Dongjin-kr/ko-reranker | 0.8509| 0.8509 | 0.8509 |
| **bge-reranker-v2-m3-ko** | **0.9123** | **0.9123** | **0.9123** |
**Top-k 3**
Bi-Encoder (Sentence Transformer)
| Model name | F1 | Recall | Precision |
|---------------------------------------|------------|------------|------------|
| paraphrase-multilingual-mpnet-base-v2 | 0.2368 | 0.4737 | 0.1579 |
| KoSimCSE-roberta | 0.3026 | 0.6053 | 0.2018 |
| Cohere embed-multilingual-v3.0 | 0.2851 | 0.5702 | 0.1901 |
| openai ada 002 | 0.3553 | 0.7105 | 0.2368 |
| multilingual-e5-large-instruct | 0.3333 | 0.6667 | 0.2222 |
| Upstage Embedding | 0.4211 | 0.8421 | 0.2807 |
| paraphrase-multilingual-MiniLM-L12-v2 | 0.2061 | 0.4123 | 0.1374 |
| openai_embed_3_small | 0.3640 | 0.7281 | 0.2427 |
| ko-sroberta-multitask | 0.2939 | 0.5877 | 0.1959 |
| openai_embed_3_large | 0.3947 | 0.7895 | 0.2632 |
| KU-HIAI-ONTHEIT-large-v1 | 0.4386 | 0.8772 | 0.2924 |
| KU-HIAI-ONTHEIT-large-v1.1 | 0.4430 | 0.8860 | 0.2953 |
| kf-deberta-multitask | 0.3158 | 0.6316 | 0.2105 |
| gte-multilingual-base | 0.4035 | 0.8070 | 0.2690 |
| KoE5 | 0.4254 | 0.8509 | 0.2836 |
| BGE-m3 | 0.4254 | 0.8508 | 0.2836 |
| bge-m3-korean | 0.3684 | 0.7368 | 0.2456 |
| **BGE-m3-ko** | **0.4517** | **0.9035** | **0.3011** |
Cross-Encoder (Reranker)
| Model name | F1 | Recall | Precision |
|---------------------------------------|------------|------------|------------|
| gte-multilingual-reranker-base | 0.4605 | 0.9211 | 0.3070 |
| jina-reranker-v2-base-multilingual | 0.4649 | 0.9298 | 0.3099 |
| bge-reranker-v2-m3 | 0.4781 | 0.9561 | 0.3187 |
| upskyy/ko-reranker-8k | 0.4781| 0.9561 | 0.3187 |
| upskyy/ko-reranker | 0.4649| 0.9298 | 0.3099 |
| mncai/bge-ko-reranker-560M | 0.0044| 0.0088 | 0.0029 |
| Dongjin-kr/ko-reranker | 0.4737| 0.9474 | 0.3158 |
| **bge-reranker-v2-m3-ko** | **0.4825** | **0.9649** | **0.3216** |
|
remember2015/test
|
remember2015
| 2025-08-19T00:50:13Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T00:49:39Z |
---
license: apache-2.0
---
# 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]
|
bimabk/6134e552-a4f8-40d3-9cfe-c1f6b4388f3a
|
bimabk
| 2025-08-19T00:49:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B",
"base_model:adapter:Qwen/Qwen2.5-7B",
"region:us"
] | null | 2025-08-19T00:49:36Z |
---
base_model: Qwen/Qwen2.5-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755562894
|
hakimjustbao
| 2025-08-19T00:48:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:48:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755564371
|
IvanJAjebu
| 2025-08-19T00:47:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:47:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TAUR-dev/M-test-rl
|
TAUR-dev
| 2025-08-19T00:40:51Z | 3 | 0 | null |
[
"safetensors",
"qwen2",
"en",
"license:mit",
"region:us"
] | null | 2025-08-14T09:22:44Z |
---
language: en
license: mit
---
# M-test-rl
## Model Details
- **Training Method**: VeRL Reinforcement Learning (RL)
- **Stage Name**: rl
- **Experiment**: test
- **RL Framework**: VeRL (Versatile Reinforcement Learning)
## Training Configuration
## Experiment Tracking
🔗 **View complete experiment details**: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__test__v1
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-test-rl")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-test-rl")
```
|
shreenithi20/fmnist-t2i-diffusion
|
shreenithi20
| 2025-08-19T00:39:59Z | 0 | 0 | null |
[
"fmnist_t2i_diffusion",
"dataset:shreenithi20/fmnist-8x8-latents",
"region:us"
] | null | 2025-08-19T00:36:40Z |
---
datasets:
- shreenithi20/fmnist-8x8-latents
---
# Fashion MNIST Text-to-Image Diffusion Model
A transformer-based diffusion model trained on Fashion MNIST latent representations for text-to-image generation.
## Model Information
- **Architecture**: Transformer-based diffusion model
- **Input**: 8×8×4 VAE latents
- **Conditioning**: Text embeddings (class labels)
- **Training Steps**: 8,500
- **Dataset**: [Fashion MNIST 8×8 Latents](https://huggingface.co/datasets/shreenithi20/fmnist-8x8-latents)
- **Framework**: PyTorch
## Checkpoints
- `model-1000.safetensors`: Early training (1k steps)
- `model-3000.safetensors`: Mid training (3k steps)
- `model-5000.safetensors`: Advanced training (5k steps)
- `model-8500.safetensors`: Final model (8.5k steps)
## Usage
```python
from transformers import AutoConfig, AutoModel
import torch
# Load model
model = AutoModel.from_pretrained("shreenithi20/fmnist-t2i-diffusion")
model.eval()
# Generate images
with torch.no_grad():
generated_latents = model.generate(
text_embeddings=class_labels,
num_inference_steps=25,
guidance_scale=7.5
)
```
## Model Architecture
- **Patch Size**: 1×1
- **Embedding Dimension**: 384
- **Transformer Layers**: 12
- **Attention Heads**: 6
- **Cross Attention Heads**: 4
- **MLP Multiplier**: 4
- **Timesteps**: Continuous (beta distribution)
- **Beta Distribution**: a=1.0, b=2.5
## Training Details
- **Learning Rate**: 1e-3 (Constant)
- **Batch Size**: 128
- **Optimizer**: AdamW
- **Mixed Precision**: Yes
- **Gradient Accumulation**: 1
## Results
The model generates high-quality Fashion MNIST images conditioned on class labels, with 8×8 latent resolution that can be decoded to 64×64 pixel images.
|
GaborMadarasz/AstroQA_mamba_epoch1_V5
|
GaborMadarasz
| 2025-08-19T00:38:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mamba",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T00:37:53Z |
---
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]
|
Noora68/lpr-0.4B
|
Noora68
| 2025-08-19T00:35:49Z | 0 | 0 | null |
[
"safetensors",
"lpr",
"biology",
"protein",
"protein classification",
"lipid binding",
"lipid binding site",
"recognition",
"en",
"base_model:EvolutionaryScale/esmc-300m-2024-12",
"base_model:finetune:EvolutionaryScale/esmc-300m-2024-12",
"license:mit",
"region:us"
] | null | 2025-08-17T03:06:41Z |
---
license: mit
language:
- en
base_model:
- EvolutionaryScale/esmc-300m-2024-12
- google-bert/bert-base-uncased
new_version: Noora68/lpr-0.4B
tags:
- biology
- protein
- protein classification
- lipid binding
- lipid binding site
- recognition
---
---
# Lipid-Protein Recognition (LPR)
we present a robust prediction tool termed Lipid-Protein Recognition (LPR)
for predicting the lipid categories that interact with proteins, utilizing
protein sequences as the only input. Using a combined model architecture by
the fusion of ESM C and BERT models, our method enables accurate and
interpretable prediction to distinguish lipid-binding signature among
the 8 major lipid categories defined by LIPID MAPS.
LPR will serve as a powerful tool to facilitate the exploration of
lipid-binding specificity and rational protein design.
---
- **Paper**: [https://...](https://....)
- **GitHub Repository**: [https://github.com/Noora68/Lipid-binding-Protein-Recognition-LPR](https://github.com/Noora68/Lipid-binding-Protein-Recognition-LPR)
- **Online Demo**: [https://colab/](https://colab/)
---
## Model Details
- **Architecture**: ESM Cambrian + BERT + classification head
- **Task**: Multi-label protein-lipid binding prediction
- **Fine-tuned from**: `ESMC_300m` + `bert-base-uncased`
- **Developed by**: Noora68
- **Framework**: PyTorch + HuggingFace Transformers
---
**Model usage workflow:**
1. Load the model and tokenizer
2. Process the input sequence (tokenize → batch → pad → mask)
3. Run inference to obtain logits → probabilities
4. Output the results and mark high-confidence categories
---
## install the latest version：
```python
pip install lpr_model==1.1.1
````
---
## Usage:
```python
from lpr_model import LPR
import torch
from torch.nn.utils.rnn import pad_sequence
from esm.tokenization import EsmSequenceTokenizer
# Set device (GPU if available, otherwise CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = EsmSequenceTokenizer()
# Default lipid type dictionary
default_dict = {
"0": "NotLipidType",
"1": "Fatty Acyl (FA)",
"2": "Prenol Lipid (PR)",
"3": "Glycerophospholipid (GP)",
"4": "Sterol Lipid (ST)",
"5": "Polyketide (PK)",
"6": "Glycerolipid (GL)",
"7": "Sphingolipid (SP)",
"8": "Saccharolipid (SL)"
}
# Load pretrained LPR model
model = LPR.from_pretrained("Noora68/lpr-0.4B").to(device)
# Example protein sequence
sequence = "MDSNFLKYLSTAPVLFTVWLSFTASFIIEANRFFPDMLYFPM"
# Tokenize the sequence -> input_ids
input_ids = torch.tensor(tokenizer.encode(sequence))
# Add batch dimension: (batch_size=1, length)
input_ids = input_ids.unsqueeze(0)
# Pad to the longest sequence in the batch
input_ids_padded = pad_sequence(input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
# Build attention mask: 1 for real tokens, 0 for padding
attention_mask = (input_ids_padded != tokenizer.pad_token_id).long()
# Move tensors to the same device as model
input_ids_padded = input_ids_padded.to(device)
attention_mask = attention_mask.to(device)
# Forward pass (no gradient needed during inference)
with torch.no_grad():
outputs = model(input_ids_padded, attention_mask)
# Convert logits to probabilities using sigmoid
probs = torch.sigmoid(outputs['logits'])
# Convert to CPU and numpy array
probs = probs.squeeze().detach().cpu().numpy()
# Print results: add a check mark if probability > 0.6
for i, p in enumerate(probs):
mark = " √" if p > 0.6 else ""
print(f"{default_dict[str(i)]:<25}: {p:.4f}{mark}")
````
## output of the above example is:
```
NotLipidType : 0.0007
Fatty Acyl (FA) : 0.1092
Prenol Lipid (PR) : 0.9178 √
Glycerophospholipid (GP) : 0.6059 √
Sterol Lipid (ST) : 0.0083
Polyketide (PK) : 0.0026
Glycerolipid (GL) : 0.0771
Sphingolipid (SP) : 0.0002
Saccharolipid (SL) : 0.0000
```
---
## Limitations
* Trained only on lipid-binding protein data and may not generalize to other functions.
* Model performance is best with sequence lengths under 500.
* Dataset size is limited compared to large-scale protein corpora.
* Model may reflect biases present in training data (e.g., under-representation of certain lipid types).
---
## Citation
If you use this model, please cite:
```bibtex
@article{your2025paper,
title={Deciphering the code of lipid binding by large language model},
author={Feitong Dong,},
journal={Bioinformatics},
year={2025}
}
```
---
## License
MIT License
---
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755562155
|
quantumxnode
| 2025-08-19T00:35:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:35:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dashawn888/MyGemmaNPC
|
dashawn888
| 2025-08-19T00:29:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T00:25:41Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="dashawn888/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755563249
|
IvanJAjebu
| 2025-08-19T00:28:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:28:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755562721
|
chooseL1fe
| 2025-08-19T00:25:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny flightless albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:25:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny flightless albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755561345
|
pempekmangedd
| 2025-08-19T00:22:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T00:22:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/xlmr_spanish_immigration
|
AnonymousCS
| 2025-08-19T00:17:56Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T00:14:48Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_spanish_immigration
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. -->
# xlmr_spanish_immigration
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2356
- Accuracy: 0.9231
- 1-f1: 0.8913
- 1-recall: 0.9535
- 1-precision: 0.8367
- Balanced Acc: 0.9308
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2922 | 1.0 | 5 | 0.1937 | 0.9308 | 0.9011 | 0.9535 | 0.8542 | 0.9365 |
| 0.0836 | 2.0 | 10 | 0.1749 | 0.9538 | 0.9302 | 0.9302 | 0.9302 | 0.9479 |
| 0.1733 | 3.0 | 15 | 0.1995 | 0.9462 | 0.9213 | 0.9535 | 0.8913 | 0.9480 |
| 0.0836 | 4.0 | 20 | 0.2356 | 0.9231 | 0.8913 | 0.9535 | 0.8367 | 0.9308 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
x2bee/Polar-32B
|
x2bee
| 2025-08-19T00:09:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T00:08:51Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
pipeline_tag: text-generation
---
# Qwen3-32B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-32B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 32.8B
- Number of Paramaters (Non-Embedding): 31.2B
- Number of Layers: 64
- Number of Attention Heads (GQA): 64 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-32B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-32B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-32B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-32B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
Cweimer05/Mccrew
|
Cweimer05
| 2025-08-19T00:08:14Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T00:08:14Z |
---
license: apache-2.0
---
|
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