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PrunaAI/seresnext50_32x4d.gluon_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:19:00Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-14T09:55:46Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir seresnext50_32x4d.gluon_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/seresnext50_32x4d.gluon_in1k-turbo-green-smashed --local-dir seresnext50_32x4d.gluon_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "seresnext50_32x4d.gluon_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "seresnext50_32x4d.gluon_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model seresnext50_32x4d.gluon_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:57Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T09:47:24Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed --local-dir resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnetv2_50d_evos.ah_in1k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnetv2_50d_evos.ah_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:56Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-07T16:59:41Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed --local-dir convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "convnext_large_mlp.clip_laion2b_augreg_ft_in1k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model convnext_large_mlp.clip_laion2b_augreg_ft_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
seongil-dn/gte-further-filtered-neg5 | seongil-dn | 2024-11-13T13:18:56Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:24811",
"loss:MultipleNegativesRankingLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-11-13T13:18:23Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:24811
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: ๋ฏผ๋ฌผ๊ณผ ๋ฐ๋ท๋ฌผ์์ ์๋ผ๋ ์ด๋ณธ ์๋ฌผ์ ์ด๋ค ์ข
๋ฅ๊ฐ ์๋์?
sentences:
- ์ด์ ์ฑ ํตํฉ์ฑ์์, R-๊ณผ์ ์ด ์ด์ ์ฑ ๋ด๋ถ์์ ํต์ตํฉ์ ์ผ์ผํค๋ ์์ธ์ด๋ค. R-๊ณผ์ ์ ์์์ ์ค์ฑ์ ํฌํ ๊ณผ์ ์ผ๋ก ๋์ ์จ๋์์ ๋์ ๋ฐ๋์
์ค์ฑ์ ์ ์์ด ์กด์ฌํ ๋ ๋ฐ์ํ๋ค. R-๊ณผ์ ์์ ์์ํต์ ๋์ ์ค์ฑ์ ์ ์์ ๋
ธ์ถ๋๋ฉฐ, ๋ถ์์ ํ ์ ๋๋ก ์ค์ฑ์๊ฐ ๋ง์ ์์ํต์ ๊ตฌ์ฑํ๋๋ฐ,
์ด๋ ๊ณง ์์ ๋ ์์ค์ ์ค์ฑ์๋ฅผ ๊ฐ์ง๋ ์์ํต์ผ๋ก ๋ถ๊ดดํ๋ค. ์ค์ฑ์ ์ ์์ ๊ทน๋๋ก ๋์ ๋งค ์ด ๋จ์ ์ผํฐ๋ฏธํฐ๋น 10์ ๋๋ ๋๋ค. ๋ค๋ฅธ ํตํฉ์ฑ
๊ณผ์ ์ผ๋ก๋ P-๊ณผ์ ๋ฐ S-๊ณผ์ ์ด ์์ผ๋ฉฐ, S-๊ณผ์ ์ ํญ์ฑ ํตํฉ์ฑ์์ ๋ํ๋๋ ๋ฐฉ์์ด๋ค.
- ๋ฏผ๋ฌผ ๋๋ ๋ฐ๋ท๋ฌผ ์์์ ์๋ผ๋ ์ด๋ณธ์ผ๋ก์, ์ธ๊ณ์ ์ด๋์ ์จ๋์ ๋๋ฆฌ ๋ถํฌํ๊ณ ์์ผ๋ฉฐ ์ฝ 15์์ 100์ข
๊ฐ๋์ด ์๋ ค์ ธ ์๋ค. ํ๊ตญ์๋
์๋ผํยท๋ฌผ์ง๊ฒฝ์ด ๋ฑ์ 5์ 5์ข
์ด ๋ถํฌํ๊ณ ์๋ค. ๊ฝ์ ์ทจ์ฐ๊ฝ์ฐจ๋ก๊ฐ ํดํ๋ ๋ชจ์์ผ๋ก ๋ฌ๋ฆฌ๋๋ฐ, ๊ฝ์ฐจ๋ก๋ ์๋ซ๋ถ๋ถ์ด ํต ๋ชจ์์ผ๋ก ํฉ์ณ์ง 2๊ฐ์
ํฌ์ด(ๅ
่พจ)๋ก ๋๋ฌ์ธ์ฌ ์๋ค. ๊ทธ๋ฌ๋, ์๊ฝ ๋๋ ์์ฑํ์์๋ 2๊ฐ ์ค์์ 1๊ฐ๋ง์ด ๋ฐ๋ฌํ๋ฉฐ ๋ค๋ฅธ ๊ฒ์ ํดํ๋์ด ์๋ค. ์๊ฝ์ ์์ผ๋ฉฐ ํฌ์ด
์์ ๋ง์ ์๊ฐ ๋ง๋ค์ด์ง๊ธฐ๋ ํ๋ค. ์์๋ด๊ทธ๋ฃจ ๋๋ ์์ํ๊ทธ๋ฃจ์ด๊ณ ๊ฝ๋ฎ์ด๋ ๋๋ถ๋ถ ๊ฝ๋ฐ์นจ๊ณผ ๊ฝ๋ถ๋ฆฌ๋ฅผ ๊ตฌ๋ณํ ์ ์์ผ๋ฉฐ ๋ณดํต 3์์ฑ์ด๋ค. ์จ๋ฐฉ์
ํ์๋ก 2-15๊ฐ์ ์ฌํผ๋ก ์ด๋ฃจ์ด์ ธ ์๋๋ฐ, ์ฌํผ์ ์๋ฉด์ ์๋ก ๊ฑฐ์ ๋จ์ด์ ธ ์์ง๋ง ๊ฝํฑ์ ์์ชฝ ๋ฉด์ด ๋ถ์ด ์์ด์ ๋ง์น ํฉ์ ์ฌํผ์ฒ๋ผ ๋ณด์ธ๋ค.
์ฌํผ ์์๋ ์ฌ๋ฌ ๊ฐ์ ๋ฐ์จ๊ฐ ์ผ์ ํ ์ฅ์ ์์ด ์ด๋์๋ ๋ฌ๋ ค ์๋ค. ์๋ถ์ ๋ฌผ์ ํ๋ฆ์ด๋ ๊ณค์ถฉ์ ์ํด์ ๋๋ ์์ ์๊ฝ์ด ์๋ฆฐ ํํ๋ก ๋ฌผ
์๋ฅผ ํ๋ฌ๋ค๋๋ค๊ฐ ์๊ฝ์ ์์ ๋จธ๋ฆฌ์ ๋ถ์ผ๋ฉด ์ด๋ฃจ์ด์ง๋ค.
- ์ผ๋ฐ์ ์ผ๋ก ์ด๋ณธ์๋ฌผ์ ๋ชฉ๋ณธ์๋ฌผ์ ๋นํด ๋งค์ฐ ์์ง๋ง, ํ์ด์(๋ฐ๋๋๊ฐ ์ํ๋ ์) ์๋ฌผ์ฒ๋ผ ์ด์ง๊ฐํ ๊ด๋ชฉ๋ณด๋ค ํฌ๊ฒ ์๋ผ๋ ์ด๋ณธ์๋ฌผ๋ ์๋ค.
- source_sentence: ๋ฏธ๊ตญ ๋
๋ฆฝ ์ ์ ์ค ์ข
๊ต์ ์ ์น์ ๋ถ๋ฆฌ์ ๋ํ ๋
ผ์๋ ์ด๋ป๊ฒ ์ด๋ฃจ์ด์ก๋์?
sentences:
- ์์ธ๋ฌ ์ฒ ํ์ด ์๋ FRP ์์ฌ๋ฅผ ์ ๊ทน ์ฌ์ฉํ์ฌ ์ฌ์ฉ์๊ฐ ์ง์ ์ธ๊ด์ ํ๋ํ๋ ๋๋ ์คํฌ๋ฉ์ด์
(Dress-formation)์ ๊ตฌํํ์๋ค. ์ด๋ก
์ธํด 3D ํ๋ฆฐํฐ๋ฅผ ์ด์ฉํด ๋ฒํผ๋ ํ๋๋ฅผ ์ง์ ๋ง๋๋ ๊ฒ์ด ๊ฐ๋ฅ์ผ ํ์๋ค.
- ๋ฏธ๊ตญ ๋
๋ฆฝ ์ ์์ ๊ณผ์ ์์ ๋
๋ฆฝ์ ์ธ์๋ฅผ ์ฑํํ ๋ฏธ๊ตญ์ธ๋ค์ ๋๋ถ๋ถ ์ฒญ๊ต๋์ ๊ฐ์ ๊ฐ์ ๊ต ์ ์์์ผ๋ฉฐ, ๋ฏธ๊ตญ ๋
๋ฆฝ ์ ์ธ์์์ ๋งํ๋ ์ฒ๋ถ์ธ๊ถ์ ๊ฐ๊ฐ์ธ์ด
์ ์๊ฒ์ ๋ฐ์ ๊ฒ์ด๋ ๋ฏฟ์์ ๊ธฐ๋ฐ์ผ๋ก ํ๊ณ ์์๋ค. ๊ทธ๋ฌ๋, ์กด ๋กํฌ์ ๊ฐ์ ์๊ตญ ๊ณ๋ชฝ์ฃผ์ ์ฌ์๊ฐ๋ค์ ์ํฅ์ ๋ฐ์๋ ์ด๋ค์ ์ข
๊ต์ ์ ์น๊ฐ ์๊ฒฉํ
๋ถ๋ฆฌ๋์ด์ผ ํ๋ค๊ณ ์๊ฐํ์๊ณ , ์ด๊ฒ์ ๋ฏธ๊ตญ ํ๋ฒ ์ 1์กฐ์ โ์ํ๋ ํน์ ์ข
๊ต๋ฅผ ๊ตญ๊ต๋ก ์ผ์ ์ ์๋คโ๊ณ ๋ช
๋ฌธํ ๋์๋ค. ๊ทธ๋ฌ๋, ์ข
๊ต์ ์ ์น๋
์ฝ๊ฒ ๋ถ๋ฆฌ๋์ง ์์๊ณ , ๋ฏธ๊ตญ ๋
๋ฆฝ ์ดํ ํํ WASP๋ผ ๋ถ๋ฆฌ๋ ๋ฐฑ์ธยท์ฅ๊ธ๋ก์น์จ๊ณยท๊ฐ์ ๊ต๋๋ ๋ฏธ๊ตญ์ ํต์ฌ ์ธ๋ ฅ์ด ๋์๋ค.
- ์ ๊ต๋ถ๋ฆฌ์ ์ถ๋ฐ์ ๋ฏธ๊ตญํ๋ฒ์ด ๋ง๋ค์ด์ง ๋ ๊ตญ๊ต๋ฅผ ๋ถ์ธํ๋๋ฐ์ ์์๋๋ค. ์ ๊ต๋ถ๋ฆฌ๋ ์์ ์ ์๋ฆฌ์ด๋ค. ์ ์น์ ์ข
๊ต๋ ๋ถ๋ฆฌ๋์ด์ผ ํ๋ค๋ ์ด์ฉ์ด
๊ฐ๋
์ ์๋ ๋ฏธ๊ตญ ํ๋ฒ ์์ 1์กฐ ๊ตํ์ ๊ตญ๊ฐ์ ๋ถ๋ฆฌ๋ผ๋ ๋ง๋ก ์ฒ์ ์ฌ์ฉ๋จ์ผ๋ก์จ ์ดํ ์ธ๊ณ์ ์ผ๋ก ์ผ๋ฐํ๋์ด ๊ฐ๋ค. ํ์ง๋ง ์์ ๋ฝ๊ณผ ๋ถ๋ฏธ๋ฅผ ์ ์ธํ
์ง์ญ์์๋ ๊ตํ โ๊ตญ๊ฐ์ ๋ถ๋ฆฌ๋ผ๋ ๋ง๋ณด๋ค โ์ ๊ต๋ถ๋ฆฌโ๊ฐ ๋ ์ผ๋ฐ์ ์ผ๋ก ์ฌ์ฉ๋๋ค.
- source_sentence: ์๋ ๊ฐ์ค ํ
์คํ
์ํฌ ์ฉ์ ์ ์ด๋ ค์ด ์ ์ ๋ฌด์์ธ๊ฐ์?
sentences:
- '์ ์ฌ๊ฐ๋ฟ์ ์ ๋์ฒด์ ๋ถํผ ๊ณต์์ ์ด์งํธ ์ 13์์กฐ(์ฝ 1850 BC)์ ์ฐ์ธ ๋ชจ์คํฌ๋ฐ ์ํ ํํผ๋ฃจ์ค๋ผ๊ณ ๋ถ๋ฆฌ๋ ๊ณ ๋ ์ด์งํธ ์ํ์์ ๋ฐ๊ฒฌ๋์๋ค:
์ฌ๊ธฐ์ "a"์ "b"๋ ๊น์ ๊ฐ๋ฟ์ ๋ฐ๋ฉด๊ณผ ์๋ฉด์ ๋ณ์ ๊ธธ์ด์ด๊ณ , "h"๋ ๋์ด์ด๋ค.
์ด์งํธ์ธ๋ค์ ๊น์ ์ ์ฌ๊ฐ๋ฟ์ ๋ถํผ๋ฅผ ์ป๋ ๊ณต์์ ์์์ง๋ง, ๋ชจ์คํฌ๋ฐ ํํผ๋ฃจ์ค์์ ์ฃผ์ด์ง ์ด ๊ณต์์ ๋ํ ์ฆ๋ช
์ ์๋ค.'
- ์๋ ๊ฐ์ค ํ
์คํ
์ํฌ ์ฉ์ ์ ์ฉ์ ๊ธฐ๊ฐ ์๊ตฌํ๋ ์กฐ์ ๋๋ฌธ์ ์๋์ ์ผ๋ก ์ด๋ ค์ด ์ฉ์ ๋ฐฉ๋ฒ์ด๋ค. ํ ์น ์ฉ์ ๊ณผ ๋ง์ฐฌ๊ฐ์ง๋ก GTAW๋ ์ผ๋ฐ์ ์ผ๋ก
๋์์ด ํ์ํ๋ค. ๋๋ถ๋ถ์ ์์ฉ์์๋ ํ์์ผ๋ก ์ฉ์ ์์ญ์ ํ๋ฌ ๊ธ์์ ์๋์ผ๋ก ๊ณต๊ธํ๊ณ ๋ค๋ฅธ ์ฉ์ ํ ์น๋ฅผ ์กฐ์ํด์ผํ๊ธฐ ๋๋ฌธ์ด๋ค. ์งง์ ์ํฌ
๊ธธ์ด๋ฅผ ์ ์งํ๋ฉด์ ์ ๊ทน๊ณผ ์์
๋ฌผ ์ฌ์ด์ ์ ์ด์ ๋ฐฉ์งํ๋ ๊ฒ๋ ์ค์ํ๋ค.
- '์ํฌ์ฉ์ ์ ์ผ์ข
.
์ต์ ์ด ์๋นํ ๋์ ํ
์คํ
๋ด์ผ๋ก ๋ถํฐ ์ํฌ๊ฐ ๋ฐ์ํด ๊ทธ ์ด๋ก ์ฌ๋ฃ๋ฅผ ๋
น์ธ๋ค.
๋ฐ์๋ ์ฉ์ ๊ณผ ๊ฐ์ด ์ค๋๊ฐ์ค๋ฅผ ์ด์ฉํ๋ค. ๋
น์ด๋ ์ฌ๋ฃ๋ฅผ ์ฒจ๊ฐํ๋๊ฒ๋ ๊ฐ๋ฅํ๋ค.
์ ๋ฐํ ์ฉ์ ์ ๊ฒฝ์ฐ์ ์ข์ ๊ณ ์ ํ์ดํ๋ ์ ๋ฐ๊ธฐ๊ธฐ์ ์ฉ์ ๋ฑ์ ์ฌ์ฉ๋๋ค.
๊ณ ์ต์ ์ ํ
์คํ
์ ์ ๊ทน์ผ๋ก ํ๊ธฐ๋๋ฌธ์ ์ ๊ทน์์ฒด์ ์๋ชจ๋ ์ ์ผ๋
์ฉ์ ๊ธ์์ ๋ถ๊ฐํ๊ธฐ ์ํด ์ผ์์ ์ฉ์ ๋ด์ ๋ค๊ณ ์์
ํด์ผํ๋ค.
์์์ ์ฌ์ฉํ๊ธฐ๋๋ฌธ์ ์๋ จ๋๊ฐ ํ์ํ๋ค. ๋น๊ต์ ๋์ด๋๋ ๋์ง๋ง, ๋น์ฒ ๊ธ์์ ๋ํ ์ฉ์ ์ ์ ์๋ ฅ์ด ๋๋ค.
์ค์ ๋ก ์๋ฃจ๋ฏธ๋์ด๋ ์คํ
์ธ๋ ์ค ์ฉ์ ์ ์ฌ์ฉํ๋ฉด, ์ํฌ๊ฐ ํ๋ผ์ฆ๋ง์ํ๋ก ๋์ด ๊ฐ์ค ์ฉ์ ์ด๋ ๋ฉ๋๊ณผ ๊ฐ์ด
๋
น์ ๋ถ๊ธฐ ๋๋ฌธ์ ๊ธฐ๋ณธ์ ์ผ๋ก ๋ง๋๊ธฐ์ฉ์ ์ค์์๋ ๊ฐ์ฅ ๊ฐ๋จํ ๋ฐฉ๋ฒ์ด๋ค.
์ ์ผํ๊ฒ ์ฉ์ ์์
์ ๋ถ๊ฝ์ด ํ์ง ์์ ํน์ง์ด ์๋ค.'
- source_sentence: ๊ณ ๋ ๋คํ ์นํฉ์ ์ฆ์์ธ ๋ฏธ๋๋ชจํ ๋
ธ ๊ณ ์ผ๋ ์ด๋ค ์ญํ ์ ํ๋์?
sentences:
- ์ 58๋ ๊ณ ์ฝ ์ฒํฉ์ ์์. ์ 1ํฉ์ ๊ณ ๋ ๋คํ ์นํฉ(ๆฏๅฟ ่ฆช็)์ ์ฆ์ ๋ฏธ๋๋ชจํ ๋
ธ ๊ณ ์ผ(ๆบๅบทๅฐ)๋ ๋ถ์ ์ ์ ์ฅ์ธ์ ์์กฐ๋ก, ๊ทธ ๊ณํต์์
๋ถ์ ์ ์๊ณต์ ๊ฐ ์ ํ๊ฐ ๋ฐฐ์ถ๋์๋ค.
- ์ง๋ฐฉ๋ ์ 815ํธ์ ์ ์ ๋ผ๋จ๋ ๋ฌด์๊ตฐ ์ผ๋ก์ ์์๋ฆฌ ์์ ๊ต์ฐจ๋ก์ ํจํ๊ตฐ ํจํ์ ๋๋๋ฆฌ ๋ฐฑ๊ณก ๊ต์ฐจ๋ก๋ฅผ ์๋ ์ ๋ผ๋จ๋์ ์ง๋ฐฉ๋์ด๋ค. ์ผ๋ก ๋๋ค๋ชฉ์
ํตํด ์ํด์๊ณ ์๋๋ก์ ์ฐ๊ฒฐ๋๋ฉฐ ๋ฌด์๊ตญ์ ๊ณตํญ์ผ๋ก ์ด์ด์ง๋ ๋๋ก์ด๊ธฐ๋ ํ๋ค.
- ์ 88๋ ๊ณ ์ฌ๊ฐ ์ฒํฉ์ ์์ ๊ณ ๋ ์ผ์ค ์น์(ๆๅบท่ฆช็)์ ์์. ๊ณ ์ฌ๊ฐ ์ฒํฉ์ ์ 2ํฉ์ ๋ฌด๋ค๋ค์นด ์น์(ๅฎๅฐ่ฆช็)์ด ๊ฐ๋ง์ฟ ๋ผ ๋ง๋ถ ์ 6๋
์ผ๊ตฐ์ ์๋ฆฌ๋ฅผ ์ฌํดํ ๋ค, ๊ทธ์ ์๋ค ์ค ํ๋๋ก ์ 7๋ ์ผ๊ตฐ์ ์ทจ์ํ ๊ณ ๋ ์ผ์ค์๊ฒ ๋ฏธ๋๋ชจํ ์ฑ์ ๋ด๋ ค ๋ฏธ๋๋ชจํ ๋
ธ ๊ณ ๋ ์ผ์ค๊ฐ ๋์๋ค. ๋จ,
๊ทธ ๋ค ๊ฐ๋ง์ฟ ๋ผ ๋ง๋ถ๊ฐ ๊ณ ๋ ์ผ์ค๋ฅผ ๊ตํ ๋ก ์ถ๋ฐฉํ๊ณ ๊ทธ๋ฅผ ๋์ ํ์ฌ ํ์ฌ์ํค ์น์(ไน
ๆ่ฆช็)์ ์ผ๊ตฐ์ผ๋ก ์ถ๋ํ๊ธฐ ์ํ์ฌ ๊ทธ ์ฌ์ ์ค๋น๋ก์ ๊ณ ๋ ์ผ์ค๋ฅผ
์น์์ ์๋ช
ํ๊ฒ ํ์ฌ, ๊ณ ๋ ์ผ์ค๋ ํฉ์กฑ์ผ๋ก ๋ณต๊ทํ์๋ค. ์ฆ, ๊ณ ์ฌ๊ฐ ๊ฒ์ง๋ ๊ณ ๋ ์ผ์ค 1๋๋ก ๋๋ฌ๋ค.
- source_sentence: ์ ์ฑ์ฒด ํ๋ฆ์ ์ด๋ป๊ฒ ๋ถํฌ๋์ด ์๋์?
sentences:
- ์ ์ฑ์ฒด ํ๋ฆ์ ๋์ฒด๋ก ๋ชจํ์ฑ์ ๊ณต์ ๊ถค๋๋ฅผ ์ค์ฌ์ผ๋ก ์ํตํ์ผ๋ก ๋ถํฌ๋์ด ์๋ค. ์ ์ฑ์ฒด์ ๋ฐ๋๋ ๋ชจํ์ฑ์ ๊ณต์ ๊ถค๋๋ก ๊ฐ์๋ก ๋์์ง๋ฉฐ, ์ง๊ตฌ๊ฐ
์ด๋ฌํ ์ ์ฑ์ฒด ํ๋ฆ์ ๊ดํตํ ๋, ์ค์ฌ์ ๋ค๊ฐ๊ฐ์๋ก ๋ ๋ง์ ์ ์ฑ์ฒด๊ฐ ์ง๊ตฌ ๋๊ธฐ ์์ผ๋ก ๋์
ํ๊ฒ ๋๋ค. ๋ฐ๋ผ์ ํ ์ ์ฑ์ฐ๊ฐ ๋ํ๋ ๋๋ ๋งค์ผ
๋ํ๋๋ ์ ์ฑ์ ๊ฐ์๊ฐ ์ฆ๊ฐํ๋ค๊ฐ ๊ฐ์ํ๋ ๊ฒฝํฅ์ ๋ค๋ค. ๊ด์ธก์ ์ผ๋ก ์ง์ํจ์์ ์ผ๋ก ์ฆ๊ฐํ๋ค๊ฐ ์ง์ํจ์์ ์ผ๋ก ๊ฐ์ํ๋ ๊ฒฝํฅ์ ๋ณด์ธ๋ค. ํ ์ ์ฑ์ฐ๊ฐ
๋ํ๋๋ ์๊ธฐ์ ์ ์ฑ๊ฐ์์ ๋ณํ๋, ์ด๋ค ์์ formula_1์์ formula_2 ์ ๊ฐ์ด ๋ํ๋ผ ์ ์๋ค. ์ ์ฑ์ ๊ฐ์๋ formula_3์ผ
๋ ์ต๋๊ฐ ๋๋๋ฐ, ์ด๊ฒ์ ๊ทน๋๊ธฐ๋ผ๊ณ ํ๋ค. ๋ํ formula_4์ ์๊ฐ ๊ท๋ชจ๋ ์ ์ฑ์ ๊ฐ์๊ฐ ํ์ฐํ๊ฒ ๋ณํ๋ ์๊ฐ ๊ท๋ชจ์ ํด๋นํ๋ค. ์ด๋ฅธ๋ฐ
์ง์ํจ์์ ์๊ฐ์ฒ๋(e-folding time scale)์ด๋ผ๊ณ ํ๋ ๊ฒ์ด๋ค. ๋จ์ํ ๋ํ๋๋ ์ ์ฑ์ ๊ฐ์๋ฅผ ์ธ๊ธฐ๋ง ํด๋ ์ด๋ฌํ ๊ฐ๋ค์ ์ธก์ ํ
์ ์์ผ๋ฉฐ, ์ด๋ก๋ถํฐ ์ง๊ตฌ ๊ณต์ ๊ถค๋์์ ๋์ฌ ์๋ ์ ์ฑ์ฒด ํ๋ฆ์ ๋ถํฌ๋ฅผ ์์ธํ ์ฐ๊ตฌํ ์ ์๋ค.
- ใG.I. ๋ธ๋ฃจ์คใ(G.I. Blues)๋ 1960๋
๋ฏธ๊ตญ ๋ฎค์ง์ปฌ ์ฝ๋ฏธ๋ ์ํ๋ก ๋
ธ๋จผ ํฐ๋ก๊ทธ๊ฐ ์ฐ์ถํ๊ณ ์๋น์ค ํ๋ ์ฌ๋ฆฌ, ์ค๋ฆฌ์ฃ ํ๋ก์ค, ๋ก๋ฒ๋ธ
์์ด๋ฒ์ค๊ฐ ์ถ์ฐํ๋ค. ์ํ๋ ํ๋ผ๋ง์ดํธ ํฝ์ฒ์ค ์คํ๋์ค์์ ์ดฌ์๋์์ผ๋ฉฐ ํ๋ ์ฌ๋ฆฌ๊ฐ ์ ๋ํ๊ธฐ ์ ์ ์ ์ ์ ํ๊ฒฝ ์ฅ๋ฉด์ด ๋
์ผ์์ ์ดฌ์๋์๋ค.
์ํ๋ ใ๋ฒ๋ผ์ด์ดํฐใ์ ์ ๊ตญ ๋ฐ์ค์คํผ์ค ์ฐจํธ์์ 2์๋ฅผ ๋ฌ์ฑํ๋ค. ๋ก๋ด ์ด์๋์ 1960๋
์ต๊ณ ์ ๋ฎค์ง์ปฌ ๋ถ๋ฌธ์์ 2์ ์์ ์์ํ๋ค.
- ์ ์ฑ์ฒด๋ ๋๋ถ๋ถ ํ์ฑ์์ ๋จ์ด์ ธ ๋์จ ๋ถ์ค๋ฌ๊ธฐ์ด๋ฉฐ, ์ผ๋ถ๋ ์ํ์ฑ์์ ๋จ์ด์ ธ ๋์จ ๋ถ์ค๋ฌ๊ธฐ๋ ์๋ค. ์ ์ฑ์ฒด๋ ํ์ฑ์ด ํด์ ๊ฐ๊น์ด ์ฌ ๋๋ง๋ค
๋ฐฉ์ถ๋๋๋ฐ, ํด์ ์ ๊ทผํ ํ์ฑ์ ์๋๋ ๋ณดํต ์ ์ญ km/s๋ฅผ ๋๋๋ค. ์ ์ฑ์ฒด๋ค์ด ํ์ฑ์์ ๋จ์ด์ ธ ๋์ฌ ๋, ๋ฐฉ์ถ ์๋๊ฐ ์กฐ๊ธ์ฉ ๋ค๋ฅด๊ณ ํ์ฑ์ด
๋ํ ์์ ํ๋ฏ๋ก ์ ์ฑ์ฒด๋ค์ ์๋ ์ฑ๋ถ์ ํ์ฑ์ ์๋์ ์ฝ๊ฐ์ฉ ์ฐจ์ด๊ฐ ์๊ธฐ๊ฒ ๋๋ค. ๊ทธ๋ฌ๋ ๊ทธ ์์ ํ์ฑ์ ์๋์ ๋นํด ์์ฃผ ์๋ค. ๊ทธ๋ฌ๋ ์ด
์์ ์๋ ์ฐจ์ด ๋๋ฌธ์ ์ ์ฑ์ฒด๋ค์ ๋์ฒด๋ก ํ์ฑ์ ๊ถค๋๋ฅผ ๋ฐ๋ผ ์ด๋์ ํ๋ ์ฝ๊ฐ์ฉ ๋ค๋ฅธ ๊ถค๋๋ฅผ ๋๊ฒ ๋์ด, ๋ง์นจ๋ด ํ์ฑ์์ ๋์จ ์ ์ฑ์ฒด๋ค์ ํ์ฑ์
๊ณต์ ๊ถค๋๋ฅผ ๋ฐ๋ผ ๋ ๋ฅผ ํ์ฑํ๊ฒ ๋๋ค. ๋๊ตฐ๋ค๋ ํ๋ฒ ๋ฐฉ์ถ๋ ์ ์ฑ์ฒด๋ ์ฃผ๋ก ๋ชฉ์ฑ๊ณผ ํด์ ์ธ๋ ฅ์ ๋ฐ๊ฒ ๋๋ฏ๋ก ๋ ๋ ์ ์ ๋ ๋์ด์ง๊ณ ๊ท ์งํ๊ฒ
๋๋ค. ์ด๊ฒ์ ์ ์ฑ์ฒด ํ๋ฆ(meteoroid stream)์ด๋ผ๊ณ ํ๋ค. ์ง๊ตฌ๊ฐ ์ ์ฑ์ฒด ํ๋ฆ์ ํฉ์ธ๊ณ ์ง๋๊ฐ ๋ ์ ์ฑ์ฐ๊ฐ ์ผ์ด๋๋ค.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 7fc06782350c1a83f88b15dd4b38ef853d3b8503 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("seongil-dn/gte-further-filtered-neg5")
# Run inference
sentences = [
'์ ์ฑ์ฒด ํ๋ฆ์ ์ด๋ป๊ฒ ๋ถํฌ๋์ด ์๋์?',
'์ ์ฑ์ฒด ํ๋ฆ์ ๋์ฒด๋ก ๋ชจํ์ฑ์ ๊ณต์ ๊ถค๋๋ฅผ ์ค์ฌ์ผ๋ก ์ํตํ์ผ๋ก ๋ถํฌ๋์ด ์๋ค. ์ ์ฑ์ฒด์ ๋ฐ๋๋ ๋ชจํ์ฑ์ ๊ณต์ ๊ถค๋๋ก ๊ฐ์๋ก ๋์์ง๋ฉฐ, ์ง๊ตฌ๊ฐ ์ด๋ฌํ ์ ์ฑ์ฒด ํ๋ฆ์ ๊ดํตํ ๋, ์ค์ฌ์ ๋ค๊ฐ๊ฐ์๋ก ๋ ๋ง์ ์ ์ฑ์ฒด๊ฐ ์ง๊ตฌ ๋๊ธฐ ์์ผ๋ก ๋์
ํ๊ฒ ๋๋ค. ๋ฐ๋ผ์ ํ ์ ์ฑ์ฐ๊ฐ ๋ํ๋ ๋๋ ๋งค์ผ ๋ํ๋๋ ์ ์ฑ์ ๊ฐ์๊ฐ ์ฆ๊ฐํ๋ค๊ฐ ๊ฐ์ํ๋ ๊ฒฝํฅ์ ๋ค๋ค. ๊ด์ธก์ ์ผ๋ก ์ง์ํจ์์ ์ผ๋ก ์ฆ๊ฐํ๋ค๊ฐ ์ง์ํจ์์ ์ผ๋ก ๊ฐ์ํ๋ ๊ฒฝํฅ์ ๋ณด์ธ๋ค. ํ ์ ์ฑ์ฐ๊ฐ ๋ํ๋๋ ์๊ธฐ์ ์ ์ฑ๊ฐ์์ ๋ณํ๋, ์ด๋ค ์์ formula_1์์ formula_2 ์ ๊ฐ์ด ๋ํ๋ผ ์ ์๋ค. ์ ์ฑ์ ๊ฐ์๋ formula_3์ผ ๋ ์ต๋๊ฐ ๋๋๋ฐ, ์ด๊ฒ์ ๊ทน๋๊ธฐ๋ผ๊ณ ํ๋ค. ๋ํ formula_4์ ์๊ฐ ๊ท๋ชจ๋ ์ ์ฑ์ ๊ฐ์๊ฐ ํ์ฐํ๊ฒ ๋ณํ๋ ์๊ฐ ๊ท๋ชจ์ ํด๋นํ๋ค. ์ด๋ฅธ๋ฐ ์ง์ํจ์์ ์๊ฐ์ฒ๋(e-folding time scale)์ด๋ผ๊ณ ํ๋ ๊ฒ์ด๋ค. ๋จ์ํ ๋ํ๋๋ ์ ์ฑ์ ๊ฐ์๋ฅผ ์ธ๊ธฐ๋ง ํด๋ ์ด๋ฌํ ๊ฐ๋ค์ ์ธก์ ํ ์ ์์ผ๋ฉฐ, ์ด๋ก๋ถํฐ ์ง๊ตฌ ๊ณต์ ๊ถค๋์์ ๋์ฌ ์๋ ์ ์ฑ์ฒด ํ๋ฆ์ ๋ถํฌ๋ฅผ ์์ธํ ์ฐ๊ตฌํ ์ ์๋ค.',
'์ ์ฑ์ฒด๋ ๋๋ถ๋ถ ํ์ฑ์์ ๋จ์ด์ ธ ๋์จ ๋ถ์ค๋ฌ๊ธฐ์ด๋ฉฐ, ์ผ๋ถ๋ ์ํ์ฑ์์ ๋จ์ด์ ธ ๋์จ ๋ถ์ค๋ฌ๊ธฐ๋ ์๋ค. ์ ์ฑ์ฒด๋ ํ์ฑ์ด ํด์ ๊ฐ๊น์ด ์ฌ ๋๋ง๋ค ๋ฐฉ์ถ๋๋๋ฐ, ํด์ ์ ๊ทผํ ํ์ฑ์ ์๋๋ ๋ณดํต ์ ์ญ km/s๋ฅผ ๋๋๋ค. ์ ์ฑ์ฒด๋ค์ด ํ์ฑ์์ ๋จ์ด์ ธ ๋์ฌ ๋, ๋ฐฉ์ถ ์๋๊ฐ ์กฐ๊ธ์ฉ ๋ค๋ฅด๊ณ ํ์ฑ์ด ๋ํ ์์ ํ๋ฏ๋ก ์ ์ฑ์ฒด๋ค์ ์๋ ์ฑ๋ถ์ ํ์ฑ์ ์๋์ ์ฝ๊ฐ์ฉ ์ฐจ์ด๊ฐ ์๊ธฐ๊ฒ ๋๋ค. ๊ทธ๋ฌ๋ ๊ทธ ์์ ํ์ฑ์ ์๋์ ๋นํด ์์ฃผ ์๋ค. ๊ทธ๋ฌ๋ ์ด ์์ ์๋ ์ฐจ์ด ๋๋ฌธ์ ์ ์ฑ์ฒด๋ค์ ๋์ฒด๋ก ํ์ฑ์ ๊ถค๋๋ฅผ ๋ฐ๋ผ ์ด๋์ ํ๋ ์ฝ๊ฐ์ฉ ๋ค๋ฅธ ๊ถค๋๋ฅผ ๋๊ฒ ๋์ด, ๋ง์นจ๋ด ํ์ฑ์์ ๋์จ ์ ์ฑ์ฒด๋ค์ ํ์ฑ์ ๊ณต์ ๊ถค๋๋ฅผ ๋ฐ๋ผ ๋ ๋ฅผ ํ์ฑํ๊ฒ ๋๋ค. ๋๊ตฐ๋ค๋ ํ๋ฒ ๋ฐฉ์ถ๋ ์ ์ฑ์ฒด๋ ์ฃผ๋ก ๋ชฉ์ฑ๊ณผ ํด์ ์ธ๋ ฅ์ ๋ฐ๊ฒ ๋๋ฏ๋ก ๋ ๋ ์ ์ ๋ ๋์ด์ง๊ณ ๊ท ์งํ๊ฒ ๋๋ค. ์ด๊ฒ์ ์ ์ฑ์ฒด ํ๋ฆ(meteoroid stream)์ด๋ผ๊ณ ํ๋ค. ์ง๊ตฌ๊ฐ ์ ์ฑ์ฒด ํ๋ฆ์ ํฉ์ธ๊ณ ์ง๋๊ฐ ๋ ์ ์ฑ์ฐ๊ฐ ์ผ์ด๋๋ค.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `learning_rate`: 7e-05
- `adam_epsilon`: 1e-07
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 7e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-07
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0026 | 1 | 0.7679 |
| 0.0052 | 2 | 0.62 |
| 0.0078 | 3 | 0.5875 |
| 0.0103 | 4 | 0.5567 |
| 0.0129 | 5 | 0.6888 |
| 0.0155 | 6 | 0.6659 |
| 0.0181 | 7 | 0.6805 |
| 0.0207 | 8 | 0.5872 |
| 0.0233 | 9 | 0.7301 |
| 0.0258 | 10 | 0.4989 |
| 0.0284 | 11 | 0.6243 |
| 0.0310 | 12 | 0.6136 |
| 0.0336 | 13 | 0.6529 |
| 0.0362 | 14 | 0.5536 |
| 0.0388 | 15 | 0.7124 |
| 0.0413 | 16 | 0.5901 |
| 0.0439 | 17 | 0.5009 |
| 0.0465 | 18 | 0.6692 |
| 0.0491 | 19 | 0.5198 |
| 0.0517 | 20 | 0.4958 |
| 0.0543 | 21 | 0.5647 |
| 0.0568 | 22 | 0.5084 |
| 0.0594 | 23 | 0.6018 |
| 0.0620 | 24 | 0.5501 |
| 0.0646 | 25 | 0.6171 |
| 0.0672 | 26 | 0.4677 |
| 0.0698 | 27 | 0.4531 |
| 0.0724 | 28 | 0.5457 |
| 0.0749 | 29 | 0.4137 |
| 0.0775 | 30 | 0.502 |
| 0.0801 | 31 | 0.3585 |
| 0.0827 | 32 | 0.4246 |
| 0.0853 | 33 | 0.4401 |
| 0.0879 | 34 | 0.448 |
| 0.0904 | 35 | 0.4464 |
| 0.0930 | 36 | 0.4546 |
| 0.0956 | 37 | 0.4943 |
| 0.0982 | 38 | 0.3874 |
| 0.1008 | 39 | 0.4109 |
| 0.1034 | 40 | 0.4747 |
| 0.1059 | 41 | 0.3218 |
| 0.1085 | 42 | 0.2444 |
| 0.1111 | 43 | 0.4396 |
| 0.1137 | 44 | 0.3343 |
| 0.1163 | 45 | 0.4269 |
| 0.1189 | 46 | 0.2613 |
| 0.1214 | 47 | 0.4472 |
| 0.1240 | 48 | 0.3737 |
| 0.1266 | 49 | 0.3696 |
| 0.1292 | 50 | 0.2962 |
| 0.1318 | 51 | 0.3207 |
| 0.1344 | 52 | 0.3006 |
| 0.1370 | 53 | 0.266 |
| 0.1395 | 54 | 0.4126 |
| 0.1421 | 55 | 0.2782 |
| 0.1447 | 56 | 0.3467 |
| 0.1473 | 57 | 0.3688 |
| 0.1499 | 58 | 0.3782 |
| 0.1525 | 59 | 0.2399 |
| 0.1550 | 60 | 0.3389 |
| 0.1576 | 61 | 0.2953 |
| 0.1602 | 62 | 0.262 |
| 0.1628 | 63 | 0.2786 |
| 0.1654 | 64 | 0.278 |
| 0.1680 | 65 | 0.2649 |
| 0.1705 | 66 | 0.2248 |
| 0.1731 | 67 | 0.2802 |
| 0.1757 | 68 | 0.1902 |
| 0.1783 | 69 | 0.2678 |
| 0.1809 | 70 | 0.2554 |
| 0.1835 | 71 | 0.31 |
| 0.1860 | 72 | 0.2631 |
| 0.1886 | 73 | 0.2766 |
| 0.1912 | 74 | 0.3062 |
| 0.1938 | 75 | 0.2294 |
| 0.1964 | 76 | 0.1803 |
| 0.1990 | 77 | 0.345 |
| 0.2016 | 78 | 0.2374 |
| 0.2041 | 79 | 0.2737 |
| 0.2067 | 80 | 0.2879 |
| 0.2093 | 81 | 0.1561 |
| 0.2119 | 82 | 0.2342 |
| 0.2145 | 83 | 0.1912 |
| 0.2171 | 84 | 0.2001 |
| 0.2196 | 85 | 0.2577 |
| 0.2222 | 86 | 0.236 |
| 0.2248 | 87 | 0.2604 |
| 0.2274 | 88 | 0.309 |
| 0.2300 | 89 | 0.2576 |
| 0.2326 | 90 | 0.254 |
| 0.2351 | 91 | 0.1699 |
| 0.2377 | 92 | 0.3595 |
| 0.2403 | 93 | 0.2516 |
| 0.2429 | 94 | 0.2495 |
| 0.2455 | 95 | 0.2182 |
| 0.2481 | 96 | 0.3665 |
| 0.2506 | 97 | 0.3084 |
| 0.2532 | 98 | 0.3122 |
| 0.2558 | 99 | 0.2174 |
| 0.2584 | 100 | 0.2536 |
| 0.2610 | 101 | 0.1953 |
| 0.2636 | 102 | 0.2979 |
| 0.2661 | 103 | 0.1005 |
| 0.2687 | 104 | 0.3461 |
| 0.2713 | 105 | 0.2068 |
| 0.2739 | 106 | 0.1989 |
| 0.2765 | 107 | 0.3092 |
| 0.2791 | 108 | 0.1499 |
| 0.2817 | 109 | 0.1323 |
| 0.2842 | 110 | 0.1536 |
| 0.2868 | 111 | 0.264 |
| 0.2894 | 112 | 0.1333 |
| 0.2920 | 113 | 0.2626 |
| 0.2946 | 114 | 0.2832 |
| 0.2972 | 115 | 0.1162 |
| 0.2997 | 116 | 0.2126 |
| 0.3023 | 117 | 0.201 |
| 0.3049 | 118 | 0.2199 |
| 0.3075 | 119 | 0.2757 |
| 0.3101 | 120 | 0.2305 |
| 0.3127 | 121 | 0.2136 |
| 0.3152 | 122 | 0.1326 |
| 0.3178 | 123 | 0.1717 |
| 0.3204 | 124 | 0.2084 |
| 0.3230 | 125 | 0.2609 |
| 0.3256 | 126 | 0.3399 |
| 0.3282 | 127 | 0.2941 |
| 0.3307 | 128 | 0.4065 |
| 0.3333 | 129 | 0.1987 |
| 0.3359 | 130 | 0.1859 |
| 0.3385 | 131 | 0.1925 |
| 0.3411 | 132 | 0.2456 |
| 0.3437 | 133 | 0.2226 |
| 0.3463 | 134 | 0.1664 |
| 0.3488 | 135 | 0.1657 |
| 0.3514 | 136 | 0.2225 |
| 0.3540 | 137 | 0.2497 |
| 0.3566 | 138 | 0.297 |
| 0.3592 | 139 | 0.2724 |
| 0.3618 | 140 | 0.1881 |
| 0.3643 | 141 | 0.2542 |
| 0.3669 | 142 | 0.2917 |
| 0.3695 | 143 | 0.1989 |
| 0.3721 | 144 | 0.1373 |
| 0.3747 | 145 | 0.1697 |
| 0.3773 | 146 | 0.2558 |
| 0.3798 | 147 | 0.1616 |
| 0.3824 | 148 | 0.2284 |
| 0.3850 | 149 | 0.1968 |
| 0.3876 | 150 | 0.1204 |
| 0.3902 | 151 | 0.2593 |
| 0.3928 | 152 | 0.3826 |
| 0.3953 | 153 | 0.2153 |
| 0.3979 | 154 | 0.2661 |
| 0.4005 | 155 | 0.2417 |
| 0.4031 | 156 | 0.234 |
| 0.4057 | 157 | 0.1506 |
| 0.4083 | 158 | 0.1771 |
| 0.4109 | 159 | 0.1616 |
| 0.4134 | 160 | 0.1898 |
| 0.4160 | 161 | 0.1969 |
| 0.4186 | 162 | 0.2431 |
| 0.4212 | 163 | 0.1992 |
| 0.4238 | 164 | 0.192 |
| 0.4264 | 165 | 0.2028 |
| 0.4289 | 166 | 0.2382 |
| 0.4315 | 167 | 0.2275 |
| 0.4341 | 168 | 0.1574 |
| 0.4367 | 169 | 0.2832 |
| 0.4393 | 170 | 0.1972 |
| 0.4419 | 171 | 0.2315 |
| 0.4444 | 172 | 0.2247 |
| 0.4470 | 173 | 0.2341 |
| 0.4496 | 174 | 0.2244 |
| 0.4522 | 175 | 0.1645 |
| 0.4548 | 176 | 0.2609 |
| 0.4574 | 177 | 0.1761 |
| 0.4599 | 178 | 0.4045 |
| 0.4625 | 179 | 0.1938 |
| 0.4651 | 180 | 0.3102 |
| 0.4677 | 181 | 0.1975 |
| 0.4703 | 182 | 0.2006 |
| 0.4729 | 183 | 0.1991 |
| 0.4755 | 184 | 0.164 |
| 0.4780 | 185 | 0.2669 |
| 0.4806 | 186 | 0.1775 |
| 0.4832 | 187 | 0.1271 |
| 0.4858 | 188 | 0.2955 |
| 0.4884 | 189 | 0.1761 |
| 0.4910 | 190 | 0.2153 |
| 0.4935 | 191 | 0.1312 |
| 0.4961 | 192 | 0.2594 |
| 0.4987 | 193 | 0.1715 |
| 0.5013 | 194 | 0.2089 |
| 0.5039 | 195 | 0.2036 |
| 0.5065 | 196 | 0.1404 |
| 0.5090 | 197 | 0.2259 |
| 0.5116 | 198 | 0.1722 |
| 0.5142 | 199 | 0.2353 |
| 0.5168 | 200 | 0.2091 |
| 0.5194 | 201 | 0.1738 |
| 0.5220 | 202 | 0.1803 |
| 0.5245 | 203 | 0.1872 |
| 0.5271 | 204 | 0.1481 |
| 0.5297 | 205 | 0.1634 |
| 0.5323 | 206 | 0.3416 |
| 0.5349 | 207 | 0.2206 |
| 0.5375 | 208 | 0.2167 |
| 0.5401 | 209 | 0.199 |
| 0.5426 | 210 | 0.1626 |
| 0.5452 | 211 | 0.3082 |
| 0.5478 | 212 | 0.2092 |
| 0.5504 | 213 | 0.2217 |
| 0.5530 | 214 | 0.2334 |
| 0.5556 | 215 | 0.1734 |
| 0.5581 | 216 | 0.2058 |
| 0.5607 | 217 | 0.2501 |
| 0.5633 | 218 | 0.3214 |
| 0.5659 | 219 | 0.1748 |
| 0.5685 | 220 | 0.2109 |
| 0.5711 | 221 | 0.1062 |
| 0.5736 | 222 | 0.3309 |
| 0.5762 | 223 | 0.1409 |
| 0.5788 | 224 | 0.1875 |
| 0.5814 | 225 | 0.2103 |
| 0.5840 | 226 | 0.1565 |
| 0.5866 | 227 | 0.2551 |
| 0.5891 | 228 | 0.2042 |
| 0.5917 | 229 | 0.1288 |
| 0.5943 | 230 | 0.1366 |
| 0.5969 | 231 | 0.1543 |
| 0.5995 | 232 | 0.2069 |
| 0.6021 | 233 | 0.2953 |
| 0.6047 | 234 | 0.2239 |
| 0.6072 | 235 | 0.2046 |
| 0.6098 | 236 | 0.1682 |
| 0.6124 | 237 | 0.2401 |
| 0.6150 | 238 | 0.2596 |
| 0.6176 | 239 | 0.1951 |
| 0.6202 | 240 | 0.2029 |
| 0.6227 | 241 | 0.1464 |
| 0.6253 | 242 | 0.1661 |
| 0.6279 | 243 | 0.1447 |
| 0.6305 | 244 | 0.1014 |
| 0.6331 | 245 | 0.1757 |
| 0.6357 | 246 | 0.1526 |
| 0.6382 | 247 | 0.1417 |
| 0.6408 | 248 | 0.1654 |
| 0.6434 | 249 | 0.2216 |
| 0.6460 | 250 | 0.287 |
| 0.6486 | 251 | 0.3283 |
| 0.6512 | 252 | 0.1765 |
| 0.6537 | 253 | 0.184 |
| 0.6563 | 254 | 0.2038 |
| 0.6589 | 255 | 0.2501 |
| 0.6615 | 256 | 0.2285 |
| 0.6641 | 257 | 0.2239 |
| 0.6667 | 258 | 0.2949 |
| 0.6693 | 259 | 0.1532 |
| 0.6718 | 260 | 0.2584 |
| 0.6744 | 261 | 0.1513 |
| 0.6770 | 262 | 0.1326 |
| 0.6796 | 263 | 0.2777 |
| 0.6822 | 264 | 0.1235 |
| 0.6848 | 265 | 0.1843 |
| 0.6873 | 266 | 0.2934 |
| 0.6899 | 267 | 0.1732 |
| 0.6925 | 268 | 0.177 |
| 0.6951 | 269 | 0.1428 |
| 0.6977 | 270 | 0.1583 |
| 0.7003 | 271 | 0.208 |
| 0.7028 | 272 | 0.1847 |
| 0.7054 | 273 | 0.1349 |
| 0.7080 | 274 | 0.1644 |
| 0.7106 | 275 | 0.214 |
| 0.7132 | 276 | 0.2338 |
| 0.7158 | 277 | 0.2421 |
| 0.7183 | 278 | 0.1836 |
| 0.7209 | 279 | 0.3185 |
| 0.7235 | 280 | 0.228 |
| 0.7261 | 281 | 0.2234 |
| 0.7287 | 282 | 0.2504 |
| 0.7313 | 283 | 0.1918 |
| 0.7339 | 284 | 0.2107 |
| 0.7364 | 285 | 0.1607 |
| 0.7390 | 286 | 0.1298 |
| 0.7416 | 287 | 0.2802 |
| 0.7442 | 288 | 0.1903 |
| 0.7468 | 289 | 0.2628 |
| 0.7494 | 290 | 0.1593 |
| 0.7519 | 291 | 0.1993 |
| 0.7545 | 292 | 0.1634 |
| 0.7571 | 293 | 0.2143 |
| 0.7597 | 294 | 0.2684 |
| 0.7623 | 295 | 0.1996 |
| 0.7649 | 296 | 0.1374 |
| 0.7674 | 297 | 0.1547 |
| 0.7700 | 298 | 0.2221 |
| 0.7726 | 299 | 0.1802 |
| 0.7752 | 300 | 0.2051 |
| 0.7778 | 301 | 0.1657 |
| 0.7804 | 302 | 0.1539 |
| 0.7829 | 303 | 0.1398 |
| 0.7855 | 304 | 0.211 |
| 0.7881 | 305 | 0.2118 |
| 0.7907 | 306 | 0.2215 |
| 0.7933 | 307 | 0.1258 |
| 0.7959 | 308 | 0.1504 |
| 0.7984 | 309 | 0.2606 |
| 0.8010 | 310 | 0.1805 |
| 0.8036 | 311 | 0.2559 |
| 0.8062 | 312 | 0.1002 |
| 0.8088 | 313 | 0.2279 |
| 0.8114 | 314 | 0.1518 |
| 0.8140 | 315 | 0.191 |
| 0.8165 | 316 | 0.1891 |
| 0.8191 | 317 | 0.1497 |
| 0.8217 | 318 | 0.1704 |
| 0.8243 | 319 | 0.1839 |
| 0.8269 | 320 | 0.132 |
| 0.8295 | 321 | 0.2276 |
| 0.8320 | 322 | 0.2594 |
| 0.8346 | 323 | 0.1868 |
| 0.8372 | 324 | 0.1443 |
| 0.8398 | 325 | 0.1967 |
| 0.8424 | 326 | 0.1041 |
| 0.8450 | 327 | 0.2678 |
| 0.8475 | 328 | 0.1805 |
| 0.8501 | 329 | 0.1565 |
| 0.8527 | 330 | 0.1672 |
| 0.8553 | 331 | 0.1416 |
| 0.8579 | 332 | 0.1541 |
| 0.8605 | 333 | 0.177 |
| 0.8630 | 334 | 0.098 |
| 0.8656 | 335 | 0.2422 |
| 0.8682 | 336 | 0.1849 |
| 0.8708 | 337 | 0.0895 |
| 0.8734 | 338 | 0.2132 |
| 0.8760 | 339 | 0.1613 |
| 0.8786 | 340 | 0.1912 |
| 0.8811 | 341 | 0.2053 |
| 0.8837 | 342 | 0.1021 |
| 0.8863 | 343 | 0.2787 |
| 0.8889 | 344 | 0.1864 |
| 0.8915 | 345 | 0.2768 |
| 0.8941 | 346 | 0.1357 |
| 0.8966 | 347 | 0.1293 |
| 0.8992 | 348 | 0.1857 |
| 0.9018 | 349 | 0.1266 |
| 0.9044 | 350 | 0.1166 |
| 0.9070 | 351 | 0.2127 |
| 0.9096 | 352 | 0.2263 |
| 0.9121 | 353 | 0.2055 |
| 0.9147 | 354 | 0.164 |
| 0.9173 | 355 | 0.0932 |
| 0.9199 | 356 | 0.1028 |
| 0.9225 | 357 | 0.142 |
| 0.9251 | 358 | 0.1558 |
| 0.9276 | 359 | 0.149 |
| 0.9302 | 360 | 0.1967 |
| 0.9328 | 361 | 0.1272 |
| 0.9354 | 362 | 0.2464 |
| 0.9380 | 363 | 0.1894 |
| 0.9406 | 364 | 0.2198 |
| 0.9432 | 365 | 0.1901 |
| 0.9457 | 366 | 0.1614 |
| 0.9483 | 367 | 0.1307 |
| 0.9509 | 368 | 0.1794 |
| 0.9535 | 369 | 0.2301 |
| 0.9561 | 370 | 0.1924 |
| 0.9587 | 371 | 0.2617 |
| 0.9612 | 372 | 0.1623 |
| 0.9638 | 373 | 0.1443 |
| 0.9664 | 374 | 0.2275 |
| 0.9690 | 375 | 0.2367 |
| 0.9716 | 376 | 0.1893 |
| 0.9742 | 377 | 0.2257 |
| 0.9767 | 378 | 0.2445 |
| 0.9793 | 379 | 0.2034 |
| 0.9819 | 380 | 0.2347 |
| 0.9845 | 381 | 0.1305 |
| 0.9871 | 382 | 0.1996 |
| 0.9897 | 383 | 0.1434 |
| 0.9922 | 384 | 0.2763 |
| 0.9948 | 385 | 0.1748 |
| 0.9974 | 386 | 0.2023 |
| 1.0 | 387 | 0.1138 |
| 1.0026 | 388 | 0.182 |
| 1.0052 | 389 | 0.2217 |
| 1.0078 | 390 | 0.1567 |
| 1.0103 | 391 | 0.1927 |
| 1.0129 | 392 | 0.2401 |
| 1.0155 | 393 | 0.21 |
| 1.0181 | 394 | 0.2667 |
| 1.0207 | 395 | 0.2306 |
| 1.0233 | 396 | 0.1865 |
| 1.0258 | 397 | 0.0838 |
| 1.0284 | 398 | 0.165 |
| 1.0310 | 399 | 0.1608 |
| 1.0336 | 400 | 0.1601 |
| 1.0362 | 401 | 0.1399 |
| 1.0388 | 402 | 0.2035 |
| 1.0413 | 403 | 0.1325 |
| 1.0439 | 404 | 0.1175 |
| 1.0465 | 405 | 0.2415 |
| 1.0491 | 406 | 0.12 |
| 1.0517 | 407 | 0.1919 |
| 1.0543 | 408 | 0.1639 |
| 1.0568 | 409 | 0.0994 |
| 1.0594 | 410 | 0.1722 |
| 1.0620 | 411 | 0.2044 |
| 1.0646 | 412 | 0.2362 |
| 1.0672 | 413 | 0.2272 |
| 1.0698 | 414 | 0.2148 |
| 1.0724 | 415 | 0.2257 |
| 1.0749 | 416 | 0.1302 |
| 1.0775 | 417 | 0.1836 |
| 1.0801 | 418 | 0.0973 |
| 1.0827 | 419 | 0.1845 |
| 1.0853 | 420 | 0.2031 |
| 1.0879 | 421 | 0.1751 |
| 1.0904 | 422 | 0.1797 |
| 1.0930 | 423 | 0.1789 |
| 1.0956 | 424 | 0.1537 |
| 1.0982 | 425 | 0.1147 |
| 1.1008 | 426 | 0.1214 |
| 1.1034 | 427 | 0.2233 |
| 1.1059 | 428 | 0.1137 |
| 1.1085 | 429 | 0.0887 |
| 1.1111 | 430 | 0.1535 |
| 1.1137 | 431 | 0.1446 |
| 1.1163 | 432 | 0.1788 |
| 1.1189 | 433 | 0.1113 |
| 1.1214 | 434 | 0.1585 |
| 1.1240 | 435 | 0.1116 |
| 1.1266 | 436 | 0.1044 |
| 1.1292 | 437 | 0.1311 |
| 1.1318 | 438 | 0.1835 |
| 1.1344 | 439 | 0.1185 |
| 1.1370 | 440 | 0.1198 |
| 1.1395 | 441 | 0.1567 |
| 1.1421 | 442 | 0.1518 |
| 1.1447 | 443 | 0.1392 |
| 1.1473 | 444 | 0.1552 |
| 1.1499 | 445 | 0.1994 |
| 1.1525 | 446 | 0.1148 |
| 1.1550 | 447 | 0.1939 |
| 1.1576 | 448 | 0.1672 |
| 1.1602 | 449 | 0.0955 |
| 1.1628 | 450 | 0.1521 |
| 1.1654 | 451 | 0.1195 |
| 1.1680 | 452 | 0.1026 |
| 1.1705 | 453 | 0.0847 |
| 1.1731 | 454 | 0.1475 |
| 1.1757 | 455 | 0.0908 |
| 1.1783 | 456 | 0.154 |
| 1.1809 | 457 | 0.1033 |
| 1.1835 | 458 | 0.1876 |
| 1.1860 | 459 | 0.1087 |
| 1.1886 | 460 | 0.1425 |
| 1.1912 | 461 | 0.2407 |
| 1.1938 | 462 | 0.1317 |
| 1.1964 | 463 | 0.0819 |
| 1.1990 | 464 | 0.1737 |
| 1.2016 | 465 | 0.1224 |
| 1.2041 | 466 | 0.1347 |
| 1.2067 | 467 | 0.1011 |
| 1.2093 | 468 | 0.071 |
| 1.2119 | 469 | 0.1006 |
| 1.2145 | 470 | 0.1182 |
| 1.2171 | 471 | 0.0642 |
| 1.2196 | 472 | 0.1359 |
| 1.2222 | 473 | 0.1492 |
| 1.2248 | 474 | 0.1573 |
| 1.2274 | 475 | 0.1393 |
| 1.2300 | 476 | 0.1126 |
| 1.2326 | 477 | 0.1377 |
| 1.2351 | 478 | 0.1398 |
| 1.2377 | 479 | 0.1944 |
| 1.2403 | 480 | 0.1248 |
| 1.2429 | 481 | 0.1594 |
| 1.2455 | 482 | 0.1209 |
| 1.2481 | 483 | 0.2041 |
| 1.2506 | 484 | 0.2128 |
| 1.2532 | 485 | 0.1167 |
| 1.2558 | 486 | 0.114 |
| 1.2584 | 487 | 0.1788 |
| 1.2610 | 488 | 0.0821 |
| 1.2636 | 489 | 0.137 |
| 1.2661 | 490 | 0.0511 |
| 1.2687 | 491 | 0.2547 |
| 1.2713 | 492 | 0.1569 |
| 1.2739 | 493 | 0.113 |
| 1.2765 | 494 | 0.1901 |
| 1.2791 | 495 | 0.0671 |
| 1.2817 | 496 | 0.086 |
| 1.2842 | 497 | 0.0904 |
| 1.2868 | 498 | 0.1443 |
| 1.2894 | 499 | 0.1084 |
| 1.2920 | 500 | 0.172 |
| 1.2946 | 501 | 0.1291 |
| 1.2972 | 502 | 0.0481 |
| 1.2997 | 503 | 0.1722 |
| 1.3023 | 504 | 0.1525 |
| 1.3049 | 505 | 0.1231 |
| 1.3075 | 506 | 0.1528 |
| 1.3101 | 507 | 0.1604 |
| 1.3127 | 508 | 0.1446 |
| 1.3152 | 509 | 0.0584 |
| 1.3178 | 510 | 0.0731 |
| 1.3204 | 511 | 0.128 |
| 1.3230 | 512 | 0.1482 |
| 1.3256 | 513 | 0.227 |
| 1.3282 | 514 | 0.1262 |
| 1.3307 | 515 | 0.3067 |
| 1.3333 | 516 | 0.1197 |
| 1.3359 | 517 | 0.1136 |
| 1.3385 | 518 | 0.1098 |
| 1.3411 | 519 | 0.173 |
| 1.3437 | 520 | 0.0962 |
| 1.3463 | 521 | 0.0972 |
| 1.3488 | 522 | 0.0965 |
| 1.3514 | 523 | 0.1618 |
| 1.3540 | 524 | 0.15 |
| 1.3566 | 525 | 0.2188 |
| 1.3592 | 526 | 0.186 |
| 1.3618 | 527 | 0.1546 |
| 1.3643 | 528 | 0.1107 |
| 1.3669 | 529 | 0.1336 |
| 1.3695 | 530 | 0.1382 |
| 1.3721 | 531 | 0.1081 |
| 1.3747 | 532 | 0.0808 |
| 1.3773 | 533 | 0.1351 |
| 1.3798 | 534 | 0.1112 |
| 1.3824 | 535 | 0.104 |
| 1.3850 | 536 | 0.0949 |
| 1.3876 | 537 | 0.0972 |
| 1.3902 | 538 | 0.1416 |
| 1.3928 | 539 | 0.2878 |
| 1.3953 | 540 | 0.1246 |
| 1.3979 | 541 | 0.1605 |
| 1.4005 | 542 | 0.2012 |
| 1.4031 | 543 | 0.1472 |
| 1.4057 | 544 | 0.0939 |
| 1.4083 | 545 | 0.1146 |
| 1.4109 | 546 | 0.0897 |
| 1.4134 | 547 | 0.1545 |
| 1.4160 | 548 | 0.1224 |
| 1.4186 | 549 | 0.134 |
| 1.4212 | 550 | 0.1823 |
| 1.4238 | 551 | 0.1636 |
| 1.4264 | 552 | 0.1333 |
| 1.4289 | 553 | 0.1029 |
| 1.4315 | 554 | 0.1856 |
| 1.4341 | 555 | 0.1147 |
| 1.4367 | 556 | 0.1698 |
| 1.4393 | 557 | 0.1202 |
| 1.4419 | 558 | 0.1402 |
| 1.4444 | 559 | 0.1612 |
| 1.4470 | 560 | 0.1623 |
| 1.4496 | 561 | 0.1503 |
| 1.4522 | 562 | 0.1027 |
| 1.4548 | 563 | 0.1812 |
| 1.4574 | 564 | 0.0991 |
| 1.4599 | 565 | 0.2166 |
| 1.4625 | 566 | 0.1367 |
| 1.4651 | 567 | 0.215 |
| 1.4677 | 568 | 0.1303 |
| 1.4703 | 569 | 0.1031 |
| 1.4729 | 570 | 0.1407 |
| 1.4755 | 571 | 0.0845 |
| 1.4780 | 572 | 0.1248 |
| 1.4806 | 573 | 0.106 |
| 1.4832 | 574 | 0.074 |
| 1.4858 | 575 | 0.1855 |
| 1.4884 | 576 | 0.0906 |
| 1.4910 | 577 | 0.1173 |
| 1.4935 | 578 | 0.0889 |
| 1.4961 | 579 | 0.1688 |
| 1.4987 | 580 | 0.1116 |
| 1.5013 | 581 | 0.1711 |
| 1.5039 | 582 | 0.1506 |
| 1.5065 | 583 | 0.0962 |
| 1.5090 | 584 | 0.1381 |
| 1.5116 | 585 | 0.1132 |
| 1.5142 | 586 | 0.1617 |
| 1.5168 | 587 | 0.1476 |
| 1.5194 | 588 | 0.0938 |
| 1.5220 | 589 | 0.1264 |
| 1.5245 | 590 | 0.1138 |
| 1.5271 | 591 | 0.0822 |
| 1.5297 | 592 | 0.091 |
| 1.5323 | 593 | 0.2277 |
| 1.5349 | 594 | 0.1301 |
| 1.5375 | 595 | 0.1917 |
| 1.5401 | 596 | 0.1524 |
| 1.5426 | 597 | 0.1021 |
| 1.5452 | 598 | 0.2273 |
| 1.5478 | 599 | 0.1036 |
| 1.5504 | 600 | 0.167 |
| 1.5530 | 601 | 0.1483 |
| 1.5556 | 602 | 0.1117 |
| 1.5581 | 603 | 0.1354 |
| 1.5607 | 604 | 0.1454 |
| 1.5633 | 605 | 0.3006 |
| 1.5659 | 606 | 0.1378 |
| 1.5685 | 607 | 0.18 |
| 1.5711 | 608 | 0.083 |
| 1.5736 | 609 | 0.2083 |
| 1.5762 | 610 | 0.0824 |
| 1.5788 | 611 | 0.1476 |
| 1.5814 | 612 | 0.1499 |
| 1.5840 | 613 | 0.1092 |
| 1.5866 | 614 | 0.2291 |
| 1.5891 | 615 | 0.1121 |
| 1.5917 | 616 | 0.0798 |
| 1.5943 | 617 | 0.0843 |
| 1.5969 | 618 | 0.1143 |
| 1.5995 | 619 | 0.1062 |
| 1.6021 | 620 | 0.209 |
| 1.6047 | 621 | 0.1556 |
| 1.6072 | 622 | 0.1828 |
| 1.6098 | 623 | 0.1107 |
| 1.6124 | 624 | 0.1827 |
| 1.6150 | 625 | 0.1885 |
| 1.6176 | 626 | 0.1606 |
| 1.6202 | 627 | 0.1561 |
| 1.6227 | 628 | 0.1256 |
| 1.6253 | 629 | 0.077 |
| 1.6279 | 630 | 0.0826 |
| 1.6305 | 631 | 0.118 |
| 1.6331 | 632 | 0.0998 |
| 1.6357 | 633 | 0.0782 |
| 1.6382 | 634 | 0.1448 |
| 1.6408 | 635 | 0.1195 |
| 1.6434 | 636 | 0.1879 |
| 1.6460 | 637 | 0.1733 |
| 1.6486 | 638 | 0.2013 |
| 1.6512 | 639 | 0.1088 |
| 1.6537 | 640 | 0.1584 |
| 1.6563 | 641 | 0.1345 |
| 1.6589 | 642 | 0.2369 |
| 1.6615 | 643 | 0.1484 |
| 1.6641 | 644 | 0.1784 |
| 1.6667 | 645 | 0.2001 |
| 1.6693 | 646 | 0.1264 |
| 1.6718 | 647 | 0.1867 |
| 1.6744 | 648 | 0.0808 |
| 1.6770 | 649 | 0.0975 |
| 1.6796 | 650 | 0.156 |
| 1.6822 | 651 | 0.076 |
| 1.6848 | 652 | 0.1397 |
| 1.6873 | 653 | 0.1591 |
| 1.6899 | 654 | 0.1405 |
| 1.6925 | 655 | 0.0888 |
| 1.6951 | 656 | 0.1066 |
| 1.6977 | 657 | 0.0932 |
| 1.7003 | 658 | 0.1541 |
| 1.7028 | 659 | 0.1614 |
| 1.7054 | 660 | 0.0826 |
| 1.7080 | 661 | 0.1334 |
| 1.7106 | 662 | 0.154 |
| 1.7132 | 663 | 0.1452 |
| 1.7158 | 664 | 0.1708 |
| 1.7183 | 665 | 0.1472 |
| 1.7209 | 666 | 0.2017 |
| 1.7235 | 667 | 0.1821 |
| 1.7261 | 668 | 0.169 |
| 1.7287 | 669 | 0.1658 |
| 1.7313 | 670 | 0.1081 |
| 1.7339 | 671 | 0.1613 |
| 1.7364 | 672 | 0.0995 |
| 1.7390 | 673 | 0.127 |
| 1.7416 | 674 | 0.1893 |
| 1.7442 | 675 | 0.1249 |
| 1.7468 | 676 | 0.1756 |
| 1.7494 | 677 | 0.1034 |
| 1.7519 | 678 | 0.1402 |
| 1.7545 | 679 | 0.099 |
| 1.7571 | 680 | 0.1466 |
| 1.7597 | 681 | 0.1805 |
| 1.7623 | 682 | 0.0954 |
| 1.7649 | 683 | 0.102 |
| 1.7674 | 684 | 0.0911 |
| 1.7700 | 685 | 0.1214 |
| 1.7726 | 686 | 0.1039 |
| 1.7752 | 687 | 0.1147 |
| 1.7778 | 688 | 0.0865 |
| 1.7804 | 689 | 0.1019 |
| 1.7829 | 690 | 0.0771 |
| 1.7855 | 691 | 0.1347 |
| 1.7881 | 692 | 0.1696 |
| 1.7907 | 693 | 0.1564 |
| 1.7933 | 694 | 0.1041 |
| 1.7959 | 695 | 0.1377 |
| 1.7984 | 696 | 0.2311 |
| 1.8010 | 697 | 0.1562 |
| 1.8036 | 698 | 0.1466 |
| 1.8062 | 699 | 0.0636 |
| 1.8088 | 700 | 0.1792 |
| 1.8114 | 701 | 0.0998 |
| 1.8140 | 702 | 0.1436 |
| 1.8165 | 703 | 0.134 |
| 1.8191 | 704 | 0.1326 |
| 1.8217 | 705 | 0.1714 |
| 1.8243 | 706 | 0.123 |
| 1.8269 | 707 | 0.119 |
| 1.8295 | 708 | 0.1803 |
| 1.8320 | 709 | 0.1752 |
| 1.8346 | 710 | 0.1116 |
| 1.8372 | 711 | 0.1199 |
| 1.8398 | 712 | 0.1444 |
| 1.8424 | 713 | 0.0871 |
| 1.8450 | 714 | 0.2385 |
| 1.8475 | 715 | 0.1565 |
| 1.8501 | 716 | 0.1185 |
| 1.8527 | 717 | 0.101 |
| 1.8553 | 718 | 0.1285 |
| 1.8579 | 719 | 0.1247 |
| 1.8605 | 720 | 0.1326 |
| 1.8630 | 721 | 0.1049 |
| 1.8656 | 722 | 0.1918 |
| 1.8682 | 723 | 0.1417 |
| 1.8708 | 724 | 0.097 |
| 1.8734 | 725 | 0.1953 |
| 1.8760 | 726 | 0.1396 |
| 1.8786 | 727 | 0.1773 |
| 1.8811 | 728 | 0.1404 |
| 1.8837 | 729 | 0.1049 |
| 1.8863 | 730 | 0.2029 |
| 1.8889 | 731 | 0.1597 |
| 1.8915 | 732 | 0.1989 |
| 1.8941 | 733 | 0.0921 |
| 1.8966 | 734 | 0.0777 |
| 1.8992 | 735 | 0.1241 |
| 1.9018 | 736 | 0.1116 |
| 1.9044 | 737 | 0.1017 |
| 1.9070 | 738 | 0.1241 |
| 1.9096 | 739 | 0.1601 |
| 1.9121 | 740 | 0.1472 |
| 1.9147 | 741 | 0.1218 |
| 1.9173 | 742 | 0.0903 |
| 1.9199 | 743 | 0.0777 |
| 1.9225 | 744 | 0.1115 |
| 1.9251 | 745 | 0.109 |
| 1.9276 | 746 | 0.1291 |
| 1.9302 | 747 | 0.1893 |
| 1.9328 | 748 | 0.1234 |
| 1.9354 | 749 | 0.25 |
| 1.9380 | 750 | 0.1475 |
| 1.9406 | 751 | 0.1574 |
| 1.9432 | 752 | 0.2231 |
| 1.9457 | 753 | 0.1341 |
| 1.9483 | 754 | 0.0776 |
| 1.9509 | 755 | 0.1712 |
| 1.9535 | 756 | 0.1629 |
| 1.9561 | 757 | 0.1751 |
| 1.9587 | 758 | 0.2061 |
| 1.9612 | 759 | 0.1329 |
| 1.9638 | 760 | 0.1284 |
| 1.9664 | 761 | 0.1937 |
| 1.9690 | 762 | 0.1458 |
| 1.9716 | 763 | 0.1317 |
| 1.9742 | 764 | 0.1141 |
| 1.9767 | 765 | 0.2299 |
| 1.9793 | 766 | 0.1455 |
| 1.9819 | 767 | 0.1535 |
| 1.9845 | 768 | 0.1123 |
| 1.9871 | 769 | 0.1963 |
| 1.9897 | 770 | 0.0977 |
| 1.9922 | 771 | 0.1847 |
| 1.9948 | 772 | 0.1192 |
| 1.9974 | 773 | 0.1481 |
| 2.0 | 774 | 0.0941 |
| 2.0026 | 775 | 0.1925 |
| 2.0052 | 776 | 0.2023 |
| 2.0078 | 777 | 0.0936 |
| 2.0103 | 778 | 0.161 |
| 2.0129 | 779 | 0.1958 |
| 2.0155 | 780 | 0.1642 |
| 2.0181 | 781 | 0.2644 |
| 2.0207 | 782 | 0.1858 |
| 2.0233 | 783 | 0.149 |
| 2.0258 | 784 | 0.0721 |
| 2.0284 | 785 | 0.1602 |
| 2.0310 | 786 | 0.083 |
| 2.0336 | 787 | 0.1192 |
| 2.0362 | 788 | 0.1133 |
| 2.0388 | 789 | 0.161 |
| 2.0413 | 790 | 0.1 |
| 2.0439 | 791 | 0.1142 |
| 2.0465 | 792 | 0.1761 |
| 2.0491 | 793 | 0.0686 |
| 2.0517 | 794 | 0.1064 |
| 2.0543 | 795 | 0.1621 |
| 2.0568 | 796 | 0.0788 |
| 2.0594 | 797 | 0.1472 |
| 2.0620 | 798 | 0.1717 |
| 2.0646 | 799 | 0.1991 |
| 2.0672 | 800 | 0.129 |
| 2.0698 | 801 | 0.177 |
| 2.0724 | 802 | 0.1344 |
| 2.0749 | 803 | 0.1433 |
| 2.0775 | 804 | 0.1261 |
| 2.0801 | 805 | 0.0999 |
| 2.0827 | 806 | 0.1114 |
| 2.0853 | 807 | 0.1265 |
| 2.0879 | 808 | 0.1632 |
| 2.0904 | 809 | 0.1247 |
| 2.0930 | 810 | 0.1392 |
| 2.0956 | 811 | 0.1489 |
| 2.0982 | 812 | 0.1131 |
| 2.1008 | 813 | 0.1147 |
| 2.1034 | 814 | 0.1957 |
| 2.1059 | 815 | 0.0873 |
| 2.1085 | 816 | 0.0996 |
| 2.1111 | 817 | 0.1317 |
| 2.1137 | 818 | 0.087 |
| 2.1163 | 819 | 0.1294 |
| 2.1189 | 820 | 0.0748 |
| 2.1214 | 821 | 0.1382 |
| 2.1240 | 822 | 0.0727 |
| 2.1266 | 823 | 0.0985 |
| 2.1292 | 824 | 0.1322 |
| 2.1318 | 825 | 0.1439 |
| 2.1344 | 826 | 0.1046 |
| 2.1370 | 827 | 0.0978 |
| 2.1395 | 828 | 0.1453 |
| 2.1421 | 829 | 0.1113 |
| 2.1447 | 830 | 0.1313 |
| 2.1473 | 831 | 0.1431 |
| 2.1499 | 832 | 0.2131 |
| 2.1525 | 833 | 0.1018 |
| 2.1550 | 834 | 0.0969 |
| 2.1576 | 835 | 0.107 |
| 2.1602 | 836 | 0.0698 |
| 2.1628 | 837 | 0.1345 |
| 2.1654 | 838 | 0.1115 |
| 2.1680 | 839 | 0.1115 |
| 2.1705 | 840 | 0.0778 |
| 2.1731 | 841 | 0.1101 |
| 2.1757 | 842 | 0.0845 |
| 2.1783 | 843 | 0.169 |
| 2.1809 | 844 | 0.0887 |
| 2.1835 | 845 | 0.1837 |
| 2.1860 | 846 | 0.0934 |
| 2.1886 | 847 | 0.1031 |
| 2.1912 | 848 | 0.2021 |
| 2.1938 | 849 | 0.1224 |
| 2.1964 | 850 | 0.0763 |
| 2.1990 | 851 | 0.1701 |
| 2.2016 | 852 | 0.1097 |
| 2.2041 | 853 | 0.1054 |
| 2.2067 | 854 | 0.1055 |
| 2.2093 | 855 | 0.0642 |
| 2.2119 | 856 | 0.0964 |
| 2.2145 | 857 | 0.0907 |
| 2.2171 | 858 | 0.0438 |
| 2.2196 | 859 | 0.1099 |
| 2.2222 | 860 | 0.0662 |
| 2.2248 | 861 | 0.1545 |
| 2.2274 | 862 | 0.1122 |
| 2.2300 | 863 | 0.0936 |
| 2.2326 | 864 | 0.1189 |
| 2.2351 | 865 | 0.1155 |
| 2.2377 | 866 | 0.2454 |
| 2.2403 | 867 | 0.0919 |
| 2.2429 | 868 | 0.1388 |
| 2.2455 | 869 | 0.1175 |
| 2.2481 | 870 | 0.1887 |
| 2.2506 | 871 | 0.156 |
| 2.2532 | 872 | 0.1174 |
| 2.2558 | 873 | 0.0975 |
| 2.2584 | 874 | 0.125 |
| 2.2610 | 875 | 0.0622 |
| 2.2636 | 876 | 0.1722 |
| 2.2661 | 877 | 0.0392 |
| 2.2687 | 878 | 0.2179 |
| 2.2713 | 879 | 0.1214 |
| 2.2739 | 880 | 0.0739 |
| 2.2765 | 881 | 0.1898 |
| 2.2791 | 882 | 0.0633 |
| 2.2817 | 883 | 0.0678 |
| 2.2842 | 884 | 0.0751 |
| 2.2868 | 885 | 0.1197 |
| 2.2894 | 886 | 0.0962 |
| 2.2920 | 887 | 0.1359 |
| 2.2946 | 888 | 0.0795 |
| 2.2972 | 889 | 0.0543 |
| 2.2997 | 890 | 0.1326 |
| 2.3023 | 891 | 0.1348 |
| 2.3049 | 892 | 0.1181 |
| 2.3075 | 893 | 0.134 |
| 2.3101 | 894 | 0.0984 |
| 2.3127 | 895 | 0.1143 |
| 2.3152 | 896 | 0.0519 |
| 2.3178 | 897 | 0.0784 |
| 2.3204 | 898 | 0.1062 |
| 2.3230 | 899 | 0.1416 |
| 2.3256 | 900 | 0.1379 |
| 2.3282 | 901 | 0.1259 |
| 2.3307 | 902 | 0.2359 |
| 2.3333 | 903 | 0.0901 |
| 2.3359 | 904 | 0.1005 |
| 2.3385 | 905 | 0.1075 |
| 2.3411 | 906 | 0.1281 |
| 2.3437 | 907 | 0.1083 |
| 2.3463 | 908 | 0.0609 |
| 2.3488 | 909 | 0.0793 |
| 2.3514 | 910 | 0.1184 |
| 2.3540 | 911 | 0.1328 |
| 2.3566 | 912 | 0.1867 |
| 2.3592 | 913 | 0.1976 |
| 2.3618 | 914 | 0.1121 |
| 2.3643 | 915 | 0.1059 |
| 2.3669 | 916 | 0.1417 |
| 2.3695 | 917 | 0.1515 |
| 2.3721 | 918 | 0.1093 |
| 2.3747 | 919 | 0.0735 |
| 2.3773 | 920 | 0.1362 |
| 2.3798 | 921 | 0.1134 |
| 2.3824 | 922 | 0.1356 |
| 2.3850 | 923 | 0.075 |
| 2.3876 | 924 | 0.0728 |
| 2.3902 | 925 | 0.1262 |
| 2.3928 | 926 | 0.2486 |
| 2.3953 | 927 | 0.1384 |
| 2.3979 | 928 | 0.1543 |
| 2.4005 | 929 | 0.1447 |
| 2.4031 | 930 | 0.1118 |
| 2.4057 | 931 | 0.0785 |
| 2.4083 | 932 | 0.1008 |
| 2.4109 | 933 | 0.0567 |
| 2.4134 | 934 | 0.1422 |
| 2.4160 | 935 | 0.1267 |
| 2.4186 | 936 | 0.1239 |
| 2.4212 | 937 | 0.1792 |
| 2.4238 | 938 | 0.1396 |
| 2.4264 | 939 | 0.1063 |
| 2.4289 | 940 | 0.0991 |
| 2.4315 | 941 | 0.12 |
| 2.4341 | 942 | 0.0853 |
| 2.4367 | 943 | 0.1595 |
| 2.4393 | 944 | 0.0952 |
| 2.4419 | 945 | 0.1225 |
| 2.4444 | 946 | 0.1013 |
| 2.4470 | 947 | 0.1431 |
| 2.4496 | 948 | 0.1648 |
| 2.4522 | 949 | 0.1057 |
| 2.4548 | 950 | 0.2071 |
| 2.4574 | 951 | 0.0992 |
| 2.4599 | 952 | 0.2224 |
| 2.4625 | 953 | 0.12 |
| 2.4651 | 954 | 0.168 |
| 2.4677 | 955 | 0.0934 |
| 2.4703 | 956 | 0.1027 |
| 2.4729 | 957 | 0.1511 |
| 2.4755 | 958 | 0.055 |
| 2.4780 | 959 | 0.1711 |
| 2.4806 | 960 | 0.1041 |
| 2.4832 | 961 | 0.0517 |
| 2.4858 | 962 | 0.1721 |
| 2.4884 | 963 | 0.0752 |
| 2.4910 | 964 | 0.1414 |
| 2.4935 | 965 | 0.0806 |
| 2.4961 | 966 | 0.1239 |
| 2.4987 | 967 | 0.1261 |
| 2.5013 | 968 | 0.1695 |
| 2.5039 | 969 | 0.115 |
| 2.5065 | 970 | 0.1079 |
| 2.5090 | 971 | 0.1031 |
| 2.5116 | 972 | 0.0872 |
| 2.5142 | 973 | 0.1775 |
| 2.5168 | 974 | 0.1164 |
| 2.5194 | 975 | 0.0926 |
| 2.5220 | 976 | 0.1239 |
| 2.5245 | 977 | 0.1012 |
| 2.5271 | 978 | 0.07 |
| 2.5297 | 979 | 0.1009 |
| 2.5323 | 980 | 0.2477 |
| 2.5349 | 981 | 0.1654 |
| 2.5375 | 982 | 0.1597 |
| 2.5401 | 983 | 0.166 |
| 2.5426 | 984 | 0.1027 |
| 2.5452 | 985 | 0.214 |
| 2.5478 | 986 | 0.0963 |
| 2.5504 | 987 | 0.1128 |
| 2.5530 | 988 | 0.1474 |
| 2.5556 | 989 | 0.1065 |
| 2.5581 | 990 | 0.1209 |
| 2.5607 | 991 | 0.132 |
| 2.5633 | 992 | 0.274 |
| 2.5659 | 993 | 0.0845 |
| 2.5685 | 994 | 0.1455 |
| 2.5711 | 995 | 0.0707 |
| 2.5736 | 996 | 0.2082 |
| 2.5762 | 997 | 0.0803 |
| 2.5788 | 998 | 0.1153 |
| 2.5814 | 999 | 0.097 |
| 2.5840 | 1000 | 0.0979 |
| 2.5866 | 1001 | 0.207 |
| 2.5891 | 1002 | 0.1084 |
| 2.5917 | 1003 | 0.0725 |
| 2.5943 | 1004 | 0.0945 |
| 2.5969 | 1005 | 0.1056 |
| 2.5995 | 1006 | 0.1284 |
| 2.6021 | 1007 | 0.1771 |
| 2.6047 | 1008 | 0.1154 |
| 2.6072 | 1009 | 0.1597 |
| 2.6098 | 1010 | 0.1019 |
| 2.6124 | 1011 | 0.1 |
| 2.6150 | 1012 | 0.1723 |
| 2.6176 | 1013 | 0.1491 |
| 2.6202 | 1014 | 0.1447 |
| 2.6227 | 1015 | 0.1142 |
| 2.6253 | 1016 | 0.0901 |
| 2.6279 | 1017 | 0.0805 |
| 2.6305 | 1018 | 0.0687 |
| 2.6331 | 1019 | 0.1021 |
| 2.6357 | 1020 | 0.1089 |
| 2.6382 | 1021 | 0.101 |
| 2.6408 | 1022 | 0.1154 |
| 2.6434 | 1023 | 0.149 |
| 2.6460 | 1024 | 0.1731 |
| 2.6486 | 1025 | 0.1902 |
| 2.6512 | 1026 | 0.106 |
| 2.6537 | 1027 | 0.1315 |
| 2.6563 | 1028 | 0.1344 |
| 2.6589 | 1029 | 0.2004 |
| 2.6615 | 1030 | 0.1629 |
| 2.6641 | 1031 | 0.1365 |
| 2.6667 | 1032 | 0.1638 |
| 2.6693 | 1033 | 0.1301 |
| 2.6718 | 1034 | 0.1822 |
| 2.6744 | 1035 | 0.0965 |
| 2.6770 | 1036 | 0.082 |
| 2.6796 | 1037 | 0.1501 |
| 2.6822 | 1038 | 0.0645 |
| 2.6848 | 1039 | 0.1261 |
| 2.6873 | 1040 | 0.2367 |
| 2.6899 | 1041 | 0.1378 |
| 2.6925 | 1042 | 0.1001 |
| 2.6951 | 1043 | 0.0973 |
| 2.6977 | 1044 | 0.1161 |
| 2.7003 | 1045 | 0.1148 |
| 2.7028 | 1046 | 0.1242 |
| 2.7054 | 1047 | 0.0867 |
| 2.7080 | 1048 | 0.1116 |
| 2.7106 | 1049 | 0.1502 |
| 2.7132 | 1050 | 0.1594 |
| 2.7158 | 1051 | 0.1459 |
| 2.7183 | 1052 | 0.1533 |
| 2.7209 | 1053 | 0.1791 |
| 2.7235 | 1054 | 0.1745 |
| 2.7261 | 1055 | 0.1128 |
| 2.7287 | 1056 | 0.1859 |
| 2.7313 | 1057 | 0.0938 |
| 2.7339 | 1058 | 0.1103 |
| 2.7364 | 1059 | 0.0907 |
| 2.7390 | 1060 | 0.0891 |
| 2.7416 | 1061 | 0.1897 |
| 2.7442 | 1062 | 0.1048 |
| 2.7468 | 1063 | 0.1777 |
| 2.7494 | 1064 | 0.1196 |
| 2.7519 | 1065 | 0.1477 |
| 2.7545 | 1066 | 0.113 |
| 2.7571 | 1067 | 0.1565 |
| 2.7597 | 1068 | 0.2063 |
| 2.7623 | 1069 | 0.0883 |
| 2.7649 | 1070 | 0.0888 |
| 2.7674 | 1071 | 0.0985 |
| 2.7700 | 1072 | 0.1242 |
| 2.7726 | 1073 | 0.1177 |
| 2.7752 | 1074 | 0.1053 |
| 2.7778 | 1075 | 0.0638 |
| 2.7804 | 1076 | 0.1103 |
| 2.7829 | 1077 | 0.0837 |
| 2.7855 | 1078 | 0.1347 |
| 2.7881 | 1079 | 0.1333 |
| 2.7907 | 1080 | 0.1697 |
| 2.7933 | 1081 | 0.1057 |
| 2.7959 | 1082 | 0.1102 |
| 2.7984 | 1083 | 0.1632 |
| 2.8010 | 1084 | 0.1295 |
| 2.8036 | 1085 | 0.1349 |
| 2.8062 | 1086 | 0.0729 |
| 2.8088 | 1087 | 0.1628 |
| 2.8114 | 1088 | 0.0935 |
| 2.8140 | 1089 | 0.1359 |
| 2.8165 | 1090 | 0.1262 |
| 2.8191 | 1091 | 0.1474 |
| 2.8217 | 1092 | 0.1248 |
| 2.8243 | 1093 | 0.1124 |
| 2.8269 | 1094 | 0.1262 |
| 2.8295 | 1095 | 0.2138 |
| 2.8320 | 1096 | 0.2028 |
| 2.8346 | 1097 | 0.122 |
| 2.8372 | 1098 | 0.1275 |
| 2.8398 | 1099 | 0.1176 |
| 2.8424 | 1100 | 0.0579 |
| 2.8450 | 1101 | 0.1725 |
| 2.8475 | 1102 | 0.1311 |
| 2.8501 | 1103 | 0.1246 |
| 2.8527 | 1104 | 0.1132 |
| 2.8553 | 1105 | 0.0998 |
| 2.8579 | 1106 | 0.1069 |
| 2.8605 | 1107 | 0.09 |
| 2.8630 | 1108 | 0.0925 |
| 2.8656 | 1109 | 0.1689 |
| 2.8682 | 1110 | 0.134 |
| 2.8708 | 1111 | 0.1002 |
| 2.8734 | 1112 | 0.1838 |
| 2.8760 | 1113 | 0.1526 |
| 2.8786 | 1114 | 0.1513 |
| 2.8811 | 1115 | 0.1702 |
| 2.8837 | 1116 | 0.101 |
| 2.8863 | 1117 | 0.1615 |
| 2.8889 | 1118 | 0.0936 |
| 2.8915 | 1119 | 0.1835 |
| 2.8941 | 1120 | 0.1015 |
| 2.8966 | 1121 | 0.0717 |
| 2.8992 | 1122 | 0.1218 |
| 2.9018 | 1123 | 0.071 |
| 2.9044 | 1124 | 0.0987 |
| 2.9070 | 1125 | 0.1109 |
| 2.9096 | 1126 | 0.12 |
| 2.9121 | 1127 | 0.1667 |
| 2.9147 | 1128 | 0.1171 |
| 2.9173 | 1129 | 0.095 |
| 2.9199 | 1130 | 0.0825 |
| 2.9225 | 1131 | 0.0654 |
| 2.9251 | 1132 | 0.1256 |
| 2.9276 | 1133 | 0.1156 |
| 2.9302 | 1134 | 0.171 |
| 2.9328 | 1135 | 0.0958 |
| 2.9354 | 1136 | 0.2148 |
| 2.9380 | 1137 | 0.1514 |
| 2.9406 | 1138 | 0.1491 |
| 2.9432 | 1139 | 0.1478 |
| 2.9457 | 1140 | 0.0833 |
| 2.9483 | 1141 | 0.0822 |
| 2.9509 | 1142 | 0.1612 |
| 2.9535 | 1143 | 0.2068 |
| 2.9561 | 1144 | 0.155 |
| 2.9587 | 1145 | 0.1877 |
| 2.9612 | 1146 | 0.1337 |
| 2.9638 | 1147 | 0.093 |
| 2.9664 | 1148 | 0.1539 |
| 2.9690 | 1149 | 0.1659 |
| 2.9716 | 1150 | 0.0969 |
| 2.9742 | 1151 | 0.1403 |
| 2.9767 | 1152 | 0.2031 |
| 2.9793 | 1153 | 0.1759 |
| 2.9819 | 1154 | 0.1254 |
| 2.9845 | 1155 | 0.1242 |
| 2.9871 | 1156 | 0.1754 |
| 2.9897 | 1157 | 0.0967 |
| 2.9922 | 1158 | 0.1602 |
| 2.9948 | 1159 | 0.1087 |
| 2.9974 | 1160 | 0.1776 |
| 3.0 | 1161 | 0.0722 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 1.1.1
- Datasets: 2.21.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
PrunaAI/vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:54Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-14T11:22:45Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed --local-dir vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "vit_large_patch14_clip_224.openai-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model vit_large_patch14_clip_224.openai before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnetaa101d.sw_in12k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:53Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T09:32:40Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnetaa101d.sw_in12k-turbo-green-smashed
huggingface-cli download PrunaAI/resnetaa101d.sw_in12k-turbo-green-smashed --local-dir resnetaa101d.sw_in12k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnetaa101d.sw_in12k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnetaa101d.sw_in12k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnetaa101d.sw_in12k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/tresnet_l.miil_in1k_448-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:51Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-19T13:22:38Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir tresnet_l.miil_in1k_448-turbo-green-smashed
huggingface-cli download PrunaAI/tresnet_l.miil_in1k_448-turbo-green-smashed --local-dir tresnet_l.miil_in1k_448-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "tresnet_l.miil_in1k_448-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "tresnet_l.miil_in1k_448-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model tresnet_l.miil_in1k_448 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/vit_base_patch8_224.dino-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:47Z | 3 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-14T10:57:38Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir vit_base_patch8_224.dino-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/vit_base_patch8_224.dino-turbo-tiny-green-smashed --local-dir vit_base_patch8_224.dino-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "vit_base_patch8_224.dino-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "vit_base_patch8_224.dino-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model vit_base_patch8_224.dino before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:46Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-14T10:58:13Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed --local-dir vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "vit_base_patch16_224.augreg_in21k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model vit_base_patch16_224.augreg_in21k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/convnext_femto_ols.d1_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:44Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-07T16:42:39Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir convnext_femto_ols.d1_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/convnext_femto_ols.d1_in1k-turbo-green-smashed --local-dir convnext_femto_ols.d1_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "convnext_femto_ols.d1_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "convnext_femto_ols.d1_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model convnext_femto_ols.d1_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnet50-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:41Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-07T13:39:41Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnet50-turbo-green-smashed
huggingface-cli download PrunaAI/resnet50-turbo-green-smashed --local-dir resnet50-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnet50-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnet50-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnet50 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/maxvit_small_tf_224.in1k-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:35Z | 4 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T05:15:08Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir maxvit_small_tf_224.in1k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/maxvit_small_tf_224.in1k-turbo-tiny-green-smashed --local-dir maxvit_small_tf_224.in1k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "maxvit_small_tf_224.in1k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "maxvit_small_tf_224.in1k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model maxvit_small_tf_224.in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:18:34Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-19T13:06:36Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed --local-dir vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "vit_base_patch32_clip_384.laion2b_ft_in12k_in1k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model vit_base_patch32_clip_384.laion2b_ft_in12k_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/mobilenetv2_120d.ra_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:33Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T05:42:32Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir mobilenetv2_120d.ra_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/mobilenetv2_120d.ra_in1k-turbo-green-smashed --local-dir mobilenetv2_120d.ra_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "mobilenetv2_120d.ra_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "mobilenetv2_120d.ra_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mobilenetv2_120d.ra_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:27Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T05:57:04Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed --local-dir mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "mobilevitv2_150.cvnets_in22k_ft_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mobilevitv2_150.cvnets_in22k_ft_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnet18.a2_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:25Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T08:45:28Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnet18.a2_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/resnet18.a2_in1k-turbo-green-smashed --local-dir resnet18.a2_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnet18.a2_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnet18.a2_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnet18.a2_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/gcresnet33ts.ra2_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:17Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-07T19:13:14Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir gcresnet33ts.ra2_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/gcresnet33ts.ra2_in1k-turbo-green-smashed --local-dir gcresnet33ts.ra2_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "gcresnet33ts.ra2_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "gcresnet33ts.ra2_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model gcresnet33ts.ra2_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnet34.a1_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:15Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T08:53:01Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnet34.a1_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/resnet34.a1_in1k-turbo-green-smashed --local-dir resnet34.a1_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnet34.a1_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnet34.a1_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnet34.a1_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/mobilenetv2_110d.ra_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:14Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T05:41:23Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir mobilenetv2_110d.ra_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/mobilenetv2_110d.ra_in1k-turbo-green-smashed --local-dir mobilenetv2_110d.ra_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "mobilenetv2_110d.ra_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "mobilenetv2_110d.ra_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mobilenetv2_110d.ra_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/resnet101.tv_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:13Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-10T09:16:01Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir resnet101.tv_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/resnet101.tv_in1k-turbo-green-smashed --local-dir resnet101.tv_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "resnet101.tv_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "resnet101.tv_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model resnet101.tv_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/ecaresnet269d.ra2_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:12Z | 3 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-19T10:38:14Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir ecaresnet269d.ra2_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/ecaresnet269d.ra2_in1k-turbo-green-smashed --local-dir ecaresnet269d.ra2_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "ecaresnet269d.ra2_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "ecaresnet269d.ra2_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ecaresnet269d.ra2_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed | PrunaAI | 2024-11-13T13:18:09Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-19T13:09:24Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed
huggingface-cli download PrunaAI/vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed --local-dir vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "vit_tiny_patch16_384.augreg_in21k_ft_in1k-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model vit_tiny_patch16_384.augreg_in21k_ft_in1k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/cerspense-zeroscope_v1-1_320s-turbo-green-smashed | PrunaAI | 2024-11-13T13:17:16Z | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-27T18:19:33Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir cerspense-zeroscope_v1-1_320s-turbo-green-smashed
huggingface-cli download PrunaAI/cerspense-zeroscope_v1-1_320s-turbo-green-smashed --local-dir cerspense-zeroscope_v1-1_320s-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "cerspense-zeroscope_v1-1_320s-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "cerspense-zeroscope_v1-1_320s-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
prompt = 'A knife is slicing a fruit'
smashed_model(prompt=prompt, height=256, width=256).frames[0]
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model cerspense/zeroscope_v1-1_320s before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/cerspense-zeroscope_v2_30x448x256-turbo-green-smashed | PrunaAI | 2024-11-13T13:17:13Z | 4 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-27T18:17:50Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.com/invite/vb6SmA3hxu)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir cerspense-zeroscope_v2_30x448x256-turbo-green-smashed
huggingface-cli download PrunaAI/cerspense-zeroscope_v2_30x448x256-turbo-green-smashed --local-dir cerspense-zeroscope_v2_30x448x256-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "cerspense-zeroscope_v2_30x448x256-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "cerspense-zeroscope_v2_30x448x256-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
prompt = 'A knife is slicing a fruit'
smashed_model(prompt=prompt, height=256, width=256).frames[0]
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model cerspense/zeroscope_v2_30x448x256 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/cerspense-zeroscope_v2_576w-turbo-green-smashed | PrunaAI | 2024-11-13T13:17:11Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:16:07Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir cerspense-zeroscope_v2_576w-turbo-green-smashed
huggingface-cli download PrunaAI/cerspense-zeroscope_v2_576w-turbo-green-smashed --local-dir cerspense-zeroscope_v2_576w-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "cerspense-zeroscope_v2_576w-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "cerspense-zeroscope_v2_576w-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='A knife is slicing a fruit', height=256, width=256).frames[0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model cerspense/zeroscope_v2_576w before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/juggernaut-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:55Z | 3 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:05:39Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir juggernaut-turbo-green-smashed
huggingface-cli download PrunaAI/juggernaut-turbo-green-smashed --local-dir juggernaut-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "juggernaut-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "juggernaut-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/274039 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/stabilityai-sdxl-turbo-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:54Z | 7 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-02-12T14:13:59Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir stabilityai-sdxl-turbo-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/stabilityai-sdxl-turbo-turbo-tiny-green-smashed --local-dir stabilityai-sdxl-turbo-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "stabilityai-sdxl-turbo-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "stabilityai-sdxl-turbo-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model stabilityai/sdxl-turbo before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:52Z | 5 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-01-29T16:39:01Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed --local-dir yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "yehiaserag-anime-pencil-diffusion-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model yehiaserag/anime-pencil-diffusion before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/sdxl-yamers-realistic-5-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:50Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:41:53Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir sdxl-yamers-realistic-5-turbo-green-smashed
huggingface-cli download PrunaAI/sdxl-yamers-realistic-5-turbo-green-smashed --local-dir sdxl-yamers-realistic-5-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "sdxl-yamers-realistic-5-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "sdxl-yamers-realistic-5-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/299716 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/newrealityxl-all-in-one-photographic-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:47Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:45:23Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir newrealityxl-all-in-one-photographic-turbo-green-smashed
huggingface-cli download PrunaAI/newrealityxl-all-in-one-photographic-turbo-green-smashed --local-dir newrealityxl-all-in-one-photographic-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "newrealityxl-all-in-one-photographic-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "newrealityxl-all-in-one-photographic-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/312982 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/segmind-SSD-1B-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:45Z | 10 | 5 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2023-11-24T16:03:23Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir segmind-SSD-1B-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/segmind-SSD-1B-turbo-tiny-green-smashed --local-dir segmind-SSD-1B-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "segmind-SSD-1B-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "segmind-SSD-1B-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model segmind/SSD-1B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/leosams-helloworld-xl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:44Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:48:42Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir leosams-helloworld-xl-turbo-green-smashed
huggingface-cli download PrunaAI/leosams-helloworld-xl-turbo-green-smashed --local-dir leosams-helloworld-xl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "leosams-helloworld-xl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "leosams-helloworld-xl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/338512 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/sdxlnijispecial-edition-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:43Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:40:47Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir sdxlnijispecial-edition-turbo-green-smashed
huggingface-cli download PrunaAI/sdxlnijispecial-edition-turbo-green-smashed --local-dir sdxlnijispecial-edition-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "sdxlnijispecial-edition-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "sdxlnijispecial-edition-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/154625?type=Model&format=SafeTensor&size=full&fp=fp16 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:41Z | 12 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-01-29T17:43:54Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed --local-dir dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "dreamlike-art-dreamlike-diffusion-1.0-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model dreamlike-art/dreamlike-diffusion-1.0 before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/wesumix-real-fantasy-5-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:39Z | 3 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:00:42Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir wesumix-real-fantasy-5-turbo-green-smashed
huggingface-cli download PrunaAI/wesumix-real-fantasy-5-turbo-green-smashed --local-dir wesumix-real-fantasy-5-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "wesumix-real-fantasy-5-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "wesumix-real-fantasy-5-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/108403 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:37Z | 3 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-25T18:55:44Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed
huggingface-cli download PrunaAI/dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed --local-dir dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "dreamlike-art-dreamlike-anime-1.0-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
prompt = 'Beautiful fruits in trees'
smashed_model(prompt=prompt, height=512, width=512)[0][0]
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model dreamlike-art/dreamlike-anime-1.0 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/zavychromaxl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:36Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:44:00Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir zavychromaxl-turbo-green-smashed
huggingface-cli download PrunaAI/zavychromaxl-turbo-green-smashed --local-dir zavychromaxl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "zavychromaxl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "zavychromaxl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/362861?type=Model&format=SafeTensor&size=full&fp=fp16 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/picxreal-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:35Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T21:25:50Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir picxreal-turbo-green-smashed
huggingface-cli download PrunaAI/picxreal-turbo-green-smashed --local-dir picxreal-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "picxreal-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "picxreal-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/272376 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/absolutereality-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:33Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:52:18Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir absolutereality-turbo-green-smashed
huggingface-cli download PrunaAI/absolutereality-turbo-green-smashed --local-dir absolutereality-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "absolutereality-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "absolutereality-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/132760?type=Model&format=SafeTensor&size=pruned&fp=fp16 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/bluepencil-xl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:32Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:36:24Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir bluepencil-xl-turbo-green-smashed
huggingface-cli download PrunaAI/bluepencil-xl-turbo-green-smashed --local-dir bluepencil-xl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "bluepencil-xl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "bluepencil-xl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/323375 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/Linaqruf-animagine-xl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:28Z | 1 | 3 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-01-29T18:18:02Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Share feedback and suggestions on the Slack of Pruna AI (Coming soon!).
## Results
**Important remarks:**
- The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
- These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json and are obtained after a hardware warmup. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...).
- You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
## Setup
You can run the smashed model with these steps:
0. Check cuda, torch, packaging requirements are installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. For packaging and torch, run `pip install packaging torch`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
```bash
pip install pruna-engine[gpu] --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir Linaqruf-animagine-xl-turbo-green-smashed
huggingface-cli download PrunaAI/Linaqruf-animagine-xl-turbo-green-smashed --local-dir Linaqruf-animagine-xl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "Linaqruf-animagine-xl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "Linaqruf-animagine-xl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model Linaqruf/animagine-xl before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:27Z | 5 | 7 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2023-11-24T03:53:37Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Share feedback and suggestions on the Slack of Pruna AI (Coming soon!).
## Results
**Important remarks:**
- The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
- These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json and are obtained after a hardware warmup. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...).
- You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
## Setup
You can run the smashed model with these steps:
0. Check cuda, torch, packaging requirements are installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. For packaging and torch, run `pip install packaging torch`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
```bash
pip install pruna-engine[gpu] --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed
huggingface-cli download PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed --local-dir stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "stabilityai-stable-diffusion-xl-base-1.0-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model stabilityai/stable-diffusion-xl-base-1.0 before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/pony-diffusion-v6-xl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:27Z | 3 | 1 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T22:27:35Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir pony-diffusion-v6-xl-turbo-green-smashed
huggingface-cli download PrunaAI/pony-diffusion-v6-xl-turbo-green-smashed --local-dir pony-diffusion-v6-xl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "pony-diffusion-v6-xl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "pony-diffusion-v6-xl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/290640 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:25Z | 8 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2023-11-23T03:25:53Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed --local-dir CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "CompVis-stable-diffusion-v1-4-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model CompVis/stable-diffusion-v1-4 before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:16:24Z | 9 | 3 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-02-12T19:29:30Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed --local-dir stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "stabilityai-stable-diffusion-xl-base-1.0-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model stabilityai/stable-diffusion-xl-base-1.0 before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/devlishphotorealism-sdxl-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:22Z | 2 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:18:26Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir devlishphotorealism-sdxl-turbo-green-smashed
huggingface-cli download PrunaAI/devlishphotorealism-sdxl-turbo-green-smashed --local-dir devlishphotorealism-sdxl-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "devlishphotorealism-sdxl-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "devlishphotorealism-sdxl-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/213449?type=Model&format=SafeTensor&size=pruned&fp=fp16 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/prompthero-openjourney-turbo-green-smashed | PrunaAI | 2024-11-13T13:16:17Z | 5 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-02-05T13:32:47Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Share feedback and suggestions on the Slack of Pruna AI (Coming soon!).
## Results
**Important remarks:**
- The quality of the model output might slightly vary compared to the base model. There might be minimal quality loss.
- These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in config.json and are obtained after a hardware warmup. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...).
- You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
## Setup
You can run the smashed model with these steps:
0. Check cuda, torch, packaging requirements are installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. For packaging and torch, run `pip install packaging torch`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take 15 minutes to install.
```bash
pip install pruna-engine[gpu] --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir prompthero-openjourney-turbo-green-smashed
huggingface-cli download PrunaAI/prompthero-openjourney-turbo-green-smashed --local-dir prompthero-openjourney-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "prompthero-openjourney-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "prompthero-openjourney-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## License
We follow the same license as the original model. Please check the license of the original model prompthero/openjourney before using this model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/ultraspice-turbo-green-smashed | PrunaAI | 2024-11-13T13:15:06Z | 5 | 1 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | 2024-03-05T23:52:43Z | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir ultraspice-turbo-green-smashed
huggingface-cli download PrunaAI/ultraspice-turbo-green-smashed --local-dir ultraspice-turbo-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "ultraspice-turbo-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "ultraspice-turbo-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input the model.
```
## Configurations
The configuration info are in `model/smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model https://civitai.com/api/download/models/342732 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
mav23/Bielik-11B-v2.0-Instruct-GGUF | mav23 | 2024-11-13T13:11:05Z | 307 | 0 | transformers | [
"transformers",
"gguf",
"finetuned",
"pl",
"arxiv:2005.01643",
"arxiv:2309.11235",
"arxiv:2006.09092",
"arxiv:2410.18565",
"base_model:speakleash/Bielik-11B-v2",
"base_model:quantized:speakleash/Bielik-11B-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-13T11:51:06Z | ---
license: apache-2.0
base_model: speakleash/Bielik-11B-v2
language:
- pl
library_name: transformers
tags:
- finetuned
inference:
parameters:
temperature: 0.2
widget:
- messages:
- role: user
content: Co przedstawia polskie godลo?
extra_gated_description: If you want to learn more about how you can use the model, please refer to our <a href="https://bielik.ai/terms/">Terms of Use</a>.
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.0-Instruct
Bielik-11B-v2.0-Instruct is a generative text model featuring 11 billion parameters.
It is an instruct fine-tuned version of the [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2).
Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH.
Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure,
specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH.
The creation and training of the Bielik-11B-v2.0-Instruct was propelled by the support of computational grant number PLG/2024/016951, conducted on the Athena and Helios supercomputer,
enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes.
As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision.
๐ฃ๏ธ Chat Arena<span style="color:red;">*</span>: https://arena.speakleash.org.pl/
<span style="color:red;">*</span>Chat Arena is a platform for testing and comparing different AI language models, allowing users to evaluate their performance and quality.
## Model
The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, synthetic instructions were generated with [Mixtral 8x22B](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) and used in training. The dataset used for training comprised over 16 million instructions, consisting of more than 8 billion tokens. The instructions varied in quality, leading to a deterioration in the modelโs performance. To counteract this while still allowing ourselves to utilize the aforementioned datasets, several improvements were introduced:
* Weighted tokens level loss - a strategy inspired by [offline reinforcement learning](https://arxiv.org/abs/2005.01643) and [C-RLFT](https://arxiv.org/abs/2309.11235)
* Adaptive learning rate inspired by the study on [Learning Rates as a Function of Batch Size](https://arxiv.org/abs/2006.09092)
* Masked prompt tokens
Bielik-11B-v2.0-Instruct has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way.
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Finetuned from:** [Bielik-11B-v2](https://huggingface.co/speakleash/Bielik-11B-v2)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
* **Model ref:** speakleash:16d24fc7821149765826d22f335eee5f
### Quantized models:
We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-11B-v2.0-Instruct model in separate repositories:
- [GGUF - Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct-GGUF)
- [GPTQ - 4bit](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct-GPTQ)
- [FP8](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct-FP8) (vLLM, SGLang - Ada Lovelace, Hopper optimized)
- [GGUF - experimental - IQ imatrix IQ1_M, IQ2_XXS, IQ3_XXS, IQ4_XS and calibrated Q4_K_M, Q5_K_M, Q6_K, Q8_0](https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct-GGUF-IQ-Imatrix)
Please note that quantized models may offer lower quality of generated answers compared to full sized variatns.
### Chat template
Bielik-11B-v2.0-Instruct uses [ChatML](https://github.com/cognitivecomputations/OpenChatML) as the prompt format.
E.g.
```
prompt = "<s><|im_start|> user\nJakie mamy pory roku?<|im_end|> \n<|im_start|> assistant\n"
completion = "W Polsce mamy 4 pory roku: wiosna, lato, jesieล i zima.<|im_end|> \n"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model_name = "speakleash/Bielik-11B-v2.0-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
messages = [
{"role": "system", "content": "Odpowiadaj krรณtko, precyzyjnie i wyลฤ
cznie w jฤzyku polskim."},
{"role": "user", "content": "Jakie mamy pory roku w Polsce?"},
{"role": "assistant", "content": "W Polsce mamy 4 pory roku: wiosna, lato, jesieล i zima."},
{"role": "user", "content": "Ktรณra jest najcieplejsza?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = input_ids.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
Fully formated input conversation by apply_chat_template from previous example:
```
<s><|im_start|> system
Odpowiadaj krรณtko, precyzyjnie i wyลฤ
cznie w jฤzyku polskim.<|im_end|>
<|im_start|> user
Jakie mamy pory roku w Polsce?<|im_end|>
<|im_start|> assistant
W Polsce mamy 4 pory roku: wiosna, lato, jesieล i zima.<|im_end|>
<|im_start|> user
Ktรณra jest najcieplejsza?<|im_end|>
```
## Evaluation
Bielik-11B-v2.0-Instruct has been evaluated on several benchmarks to assess its performance across various tasks and languages. These benchmarks include:
1. Open PL LLM Leaderboard
2. Open LLM Leaderboard
3. Polish MT-Bench
4. Polish EQ-Bench (Emotional Intelligence Benchmark)
5. MixEval
The following sections provide detailed results for each of these benchmarks, demonstrating the model's capabilities in both Polish and English language tasks.
### Open PL LLM Leaderboard
Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Average column is an average score among all tasks normalized by baseline scores.
| Model | Parameters (B)| Average |
|---------------------------------|------------|---------|
| Meta-Llama-3.1-405B-Instruct-FP8,API | 405 | 69.44 |
| Mistral-Large-Instruct-2407 | 123 | 69.11 |
| Qwen2-72B-Instruct | 72 | 65.87 |
| Bielik-11B-v2.2-Instruct | 11 | 65.57 |
| Meta-Llama-3.1-70B-Instruct | 70 | 65.49 |
| Bielik-11B-v2.1-Instruct | 11 | 65.45 |
| Mixtral-8x22B-Instruct-v0.1 | 141 | 65.23 |
| **Bielik-11B-v2.0-Instruct** | **11** | **64.98** |
| Meta-Llama-3-70B-Instruct | 70 | 64.45 |
| Athene-70B | 70 | 63.65 |
| WizardLM-2-8x22B | 141 | 62.35 |
| Qwen1.5-72B-Chat | 72 | 58.67 |
| Qwen2-57B-A14B-Instruct | 57 | 56.89 |
| glm-4-9b-chat | 9 | 56.61 |
| aya-23-35B | 35 | 56.37 |
| Phi-3.5-MoE-instruct | 41.9 | 56.34 |
| openchat-3.5-0106-gemma | 7 | 55.69 |
| Mistral-Nemo-Instruct-2407 | 12 | 55.27 |
| SOLAR-10.7B-Instruct-v1.0 | 10.7 | 55.24 |
| Mixtral-8x7B-Instruct-v0.1 | 46.7 | 55.07 |
| Bielik-7B-Instruct-v0.1 | 7 | 44.70 |
| trurl-2-13b-academic | 13 | 36.28 |
| trurl-2-7b | 7 | 26.93 |
The results from the Open PL LLM Leaderboard demonstrate the exceptional performance of Bielik-11B-v2.0-Instruct:
1. Superior performance in its class: Bielik-11B-v2.0-Instruct outperforms all other models with less than 70B parameters. This is a significant achievement, showcasing its efficiency and effectiveness despite having fewer parameters than many competitors.
2. Competitive with larger models: with a score of 64.98, Bielik-11B-v2.0-Instruct performs on par with models in the 70B parameter range. This indicates that it achieves comparable results to much larger models, demonstrating its advanced architecture and training methodology.
3. Substantial improvement over previous version: the model shows a marked improvement over its predecessor, Bielik-7B-Instruct-v0.1, which scored 43.64. This leap in performance highlights the successful enhancements and optimizations implemented in this newer version.
4. Leading position for Polish language models: in the context of Polish language models, Bielik-11B-v2 Instruct stands out as a leader. There are no other competitive models specifically tailored for the Polish language that match its performance, making it a crucial resource for Polish NLP tasks.
These results underscore Bielik-11B-v2.0-Instruct's position as a state-of-the-art model for Polish language processing, offering high performance with relatively modest computational requirements.
#### Open PL LLM Leaderboard - Generative Tasks Performance
This section presents a focused comparison of generative Polish language task performance between Bielik models and GPT-3.5. The evaluation is limited to generative tasks due to the constraints of assessing OpenAI models. The comprehensive nature and associated costs of the benchmark explain the limited number of models evaluated.
| Model | Parameters (B) | Average g |
|-------------------------------|----------------|---------------|
| Bielik-11B-v2.1-Instruct | 11 | 66.58 |
| Bielik-11B-v2.2-Instruct | 11 | 66.11 |
| **Bielik-11B-v2.0-Instruct** | 11 | **65.58** |
| gpt-3.5-turbo-instruct | Unknown | 55.65 |
The performance variation among Bielik versions is minimal, indicating consistent quality across iterations. Bielik-11B-v2.1-Instruct demonstrates an impressive 17.8% performance advantage over GPT-3.5.
### Open LLM Leaderboard
The Open LLM Leaderboard evaluates models on various English language tasks, providing insights into the model's performance across different linguistic challenges.
| Model | AVG | arc_challenge | hellaswag | truthfulqa_mc2 | mmlu | winogrande | gsm8k |
|--------------------------|-------|---------------|-----------|----------------|-------|------------|-------|
| Bielik-11B-v2.2-Instruct | 69.86 | 59.90 | 80.16 | 58.34 | 64.34 | 75.30 | 81.12 |
| Bielik-11B-v2.1-Instruct | 69.82 | 59.56 | 80.20 | 59.35 | 64.18 | 75.06 | 80.59 |
| **Bielik-11B-v2.0-Instruct** | **68.04** | 58.62 | 78.65 | 54.65 | 63.71 | 76.32 | 76.27 |
| Bielik-11B-v2 | 65.87 | 60.58 | 79.84 | 46.13 | 63.06 | 77.82 | 67.78 |
| Mistral-7B-Instruct-v0.2 | 65.71 | 63.14 | 84.88 | 68.26 | 60.78 | 77.19 | 40.03 |
| Bielik-7B-Instruct-v0.1 | 51.26 | 47.53 | 68.91 | 49.47 | 46.18 | 65.51 | 29.95 |
Bielik-11B-v2.0-Instruct shows impressive performance on English language tasks:
1. Improvement over its base model (2-point increase).
2. Substantial 16-point improvement over Bielik-7B-Instruct-v0.1.
These results demonstrate Bielik-11B-v2.0-Instruct's versatility in both Polish and English, highlighting the effectiveness of its instruction tuning process.
### Polish MT-Bench
The Bielik-11B-v2.0-Instruct (16 bit) model was also evaluated using the MT-Bench benchmark. The quality of the model was evaluated using the English version (original version without modifications) and the Polish version created by Speakleash (tasks and evaluation in Polish, the content of the tasks was also changed to take into account the context of the Polish language).
#### MT-Bench English
| Model | Score |
|-----------------|----------|
| Bielik-11B-v2.1 | 8.537500 |
| Bielik-11B-v2.2 | 8.390625 |
| **Bielik-11B-v2.0** | **8.159375** |
#### MT-Bench Polish
| Model | Parameters (B) | Score |
|-------------------------------------|----------------|----------|
| Qwen2-72B-Instruct | 72 | 8.775000 |
| Mistral-Large-Instruct-2407 (123B) | 123 | 8.662500 |
| gemma-2-27b-it | 27 | 8.618750 |
| Mixtral-8x22b | 141 | 8.231250 |
| Meta-Llama-3.1-405B-Instruct | 405 | 8.168750 |
| Meta-Llama-3.1-70B-Instruct | 70 | 8.150000 |
| Bielik-11B-v2.2-Instruct | 11 | 8.115625 |
| Bielik-11B-v2.1-Instruct | 11 | 7.996875 |
| gpt-3.5-turbo | Unknown | 7.868750 |
| Mixtral-8x7b | 46.7 | 7.637500 |
| **Bielik-11B-v2.0-Instruct** | **11** | **7.562500** |
| Mistral-Nemo-Instruct-2407 | 12 | 7.368750 |
| openchat-3.5-0106-gemma | 7 | 6.812500 |
| Mistral-7B-Instruct-v0.2 | 7 | 6.556250 |
| Meta-Llama-3.1-8B-Instruct | 8 | 6.556250 |
| Bielik-7B-Instruct-v0.1 | 7 | 6.081250 |
| Mistral-7B-Instruct-v0.3 | 7 | 5.818750 |
| Polka-Mistral-7B-SFT | 7 | 4.518750 |
| trurl-2-7b | 7 | 2.762500 |
For more information - answers to test tasks and values in each category, visit the [MT-Bench PL](https://huggingface.co/spaces/speakleash/mt-bench-pl) website.
### Polish EQ-Bench
[Polish Emotional Intelligence Benchmark for LLMs](https://huggingface.co/spaces/speakleash/polish_eq-bench)
| Model | Parameters (B) | Score |
|-------------------------------|--------|-------|
| Mistral-Large-Instruct-2407 | 123 | 78.07 |
| Meta-Llama-3.1-405B-Instruct-FP8 | 405 | 77.23 |
| gpt-4o-2024-08-06 | ? | 75.15 |
| gpt-4-turbo-2024-04-09 | ? | 74.59 |
| Meta-Llama-3.1-70B-Instruct | 70 | 72.53 |
| Qwen2-72B-Instruct | 72 | 71.23 |
| Meta-Llama-3-70B-Instruct | 70 | 71.21 |
| gpt-4o-mini-2024-07-18 | ? | 71.15 |
| WizardLM-2-8x22B | 141 | 69.56 |
| Bielik-11B-v2.2-Instruct | 11 | 69.05 |
| **Bielik-11B-v2.0-Instruct** | **11** | **68.24** |
| Qwen1.5-72B-Chat | 72 | 68.03 |
| Mixtral-8x22B-Instruct-v0.1 | 141 | 67.63 |
| Bielik-11B-v2.1-Instruct | 11 | 60.07 |
| Qwen1.5-32B-Chat | 32 | 59.63 |
| openchat-3.5-0106-gemma | 7 | 59.58 |
| aya-23-35B | 35 | 58.41 |
| gpt-3.5-turbo | ? | 57.7 |
| Qwen2-57B-A14B-Instruct | 57 | 57.64 |
| Mixtral-8x7B-Instruct-v0.1 | 47 | 57.61 |
| SOLAR-10.7B-Instruct-v1.0 | 10.7 | 55.21 |
| Mistral-7B-Instruct-v0.2 | 7 | 47.02 |
### MixEval
MixEval is a ground-truth-based English benchmark designed to evaluate Large Language Models (LLMs) efficiently and effectively. Key features of MixEval include:
1. Derived from off-the-shelf benchmark mixtures
2. Highly capable model ranking with a 0.96 correlation to Chatbot Arena
3. Local and quick execution, requiring only 6% of the time and cost compared to running MMLU
This benchmark provides a robust and time-efficient method for assessing LLM performance, making it a valuable tool for ongoing model evaluation and comparison.
| Model | MixEval | MixEval-Hard |
|-------------------------------|---------|--------------|
| Bielik-11B-v2.1-Instruct | 74.55 | 45.00 |
| Bielik-11B-v2.2-Instruct | 72.35 | 39.65 |
| **Bielik-11B-v2.0-Instruct** | **72.10** | **40.20** |
| Mistral-7B-Instruct-v0.2 | 70.00 | 36.20 |
The results show that Bielik-11B-v2.0-Instruct performs well on the MixEval benchmark, achieving a score of 72.10 on the standard MixEval and 40.20 on MixEval-Hard. Notably, Bielik-11B-v2.0-Instruct significantly outperforms Mistral-7B-Instruct-v0.2 on both metrics, demonstrating its improved capabilities despite being based on a similar architecture.
### Chat Arena PL
Chat Arena PL is a human-evaluated benchmark that provides a direct comparison of model performance through head-to-head battles. Unlike the automated benchmarks mentioned above, this evaluation relies on human judgment to assess the quality and effectiveness of model responses. The results offer valuable insights into how different models perform in real-world, conversational scenarios as perceived by human evaluators.
Results accessed on 2024-08-26.
| # | Model | Battles | Won | Lost | Draws | Win % | ELO |
|---|-------|-------|---------|-----------|--------|-------------|-----|
| 1 | Bielik-11B-v2.2-Instruct | 92 | 72 | 14 | 6 | 83.72% | 1234 |
| 2 | Bielik-11B-v2.1-Instruct | 240 | 171 | 50 | 19 | 77.38% | 1174 |
| 3 | gpt-4o-mini | 639 | 402 | 117 | 120 | 77.46% | 1141 |
| 4 | Mistral Large 2 (2024-07) | 324 | 188 | 69 | 67 | 73.15% | 1125 |
| 5 | Llama-3.1-405B | 548 | 297 | 144 | 107 | 67.35% | 1090 |
| 6 | **Bielik-11B-v2.0-Instruct** | 1289 | 695 | 352 | 242 | 66.38% | 1059 |
| 7 | Llama-3.1-70B | 498 | 221 | 187 | 90 | 54.17% | 1033 |
| 8 | Bielik-1-7B | 2041 | 1029 | 638 | 374 | 61.73% | 1020 |
| 9 | Mixtral-8x22B-v0.1 | 432 | 166 | 167 | 99 | 49.85% | 1018 |
| 10 | Qwen2-72B | 451 | 179 | 177 | 95 | 50.28% | 1011 |
| 11 | gpt-3.5-turbo | 2186 | 1007 | 731 | 448 | 57.94% | 1008 |
| 12 | Llama-3.1-8B | 440 | 155 | 227 | 58 | 40.58% | 975 |
| 13 | Mixtral-8x7B-v0.1 | 1997 | 794 | 804 | 399 | 49.69% | 973 |
| 14 | Llama-3-70b | 2008 | 733 | 909 | 366 | 44.64% | 956 |
| 15 | Mistral Nemo (2024-07) | 301 | 84 | 164 | 53 | 33.87% | 954 |
| 16 | Llama-3-8b | 1911 | 473 | 1091 | 347 | 30.24% | 909 |
| 17 | gemma-7b-it | 1928 | 418 | 1221 | 289 | 25.5% | 888 |
## Limitations and Biases
Bielik-11B-v2.0-Instruct is a quick demonstration that the base model can be easily fine-tuned to achieve compelling and promising performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community in ways to make the model respect guardrails, allowing for deployment in environments requiring moderated outputs.
Bielik-11B-v2.0-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-11B-v2.0-Instruct was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs.
## Citation
Please cite this model using the following format:
```
@misc{Bielik11Bv20i,
title = {Bielik-11B-v2.0-Instruct model card},
author = {Ociepa, Krzysztof and Flis, ลukasz and Kinas, Remigiusz and Gwoลบdziej, Adrian and Wrรณbel, Krzysztof and {SpeakLeash Team} and {Cyfronet Team}},
year = {2024},
url = {https://huggingface.co/speakleash/Bielik-11B-v2.0-Instruct},
note = {Accessed: 2024-09-10}, % change this date
urldate = {2024-09-10} % change this date
}
@unpublished{Bielik11Bv20a,
author = {Ociepa, Krzysztof and Flis, ลukasz and Kinas, Remigiusz and Gwoลบdziej, Adrian and Wrรณbel, Krzysztof},
title = {Bielik: A Family of Large Language Models for the Polish Language - Development, Insights, and Evaluation},
year = {2024},
}
@misc{ociepa2024bielik7bv01polish,
title={Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation},
author={Krzysztof Ociepa and ลukasz Flis and Krzysztof Wrรณbel and Adrian Gwoลบdziej and Remigiusz Kinas},
year={2024},
eprint={2410.18565},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.18565},
}
```
## Responsible for training the model
* [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training
* [ลukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - conceptualizing and coordinating DPO training, data preparation
* [Adrian Gwoลบdziej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data preparation and ensuring data quality
* [Krzysztof Wrรณbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks
The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model:
[Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/),
[Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/),
[Paweล Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/),
[Szymon Baczyลski](https://www.linkedin.com/in/szymon-baczynski/),
[Jacek Chwiลa](https://www.linkedin.com/in/jacek-chwila/),
[Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/),
[Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/),
[Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/),
[Kacper Milan](https://www.linkedin.com/in/kacper-milan/),
[Jan Sowa](https://www.linkedin.com/in/janpiotrsowa/),
[Len Krawczyk](https://www.linkedin.com/in/magdalena-krawczyk-7810942ab/),
[Marta Seidler](https://www.linkedin.com/in/marta-seidler-751102259/),
[Agnieszka Ratajska](https://www.linkedin.com/in/agnieszka-ratajska/),
[Krzysztof Koziarek](https://www.linkedin.com/in/krzysztofkoziarek/),
[Szymon Pepliลski](http://linkedin.com/in/szymonpeplinski/),
[Zuzanna Dabiฤ](https://www.linkedin.com/in/zuzanna-dabic/),
[Filip Bogacz](https://linkedin.com/in/Fibogacci),
[Agnieszka Kosiak](https://www.linkedin.com/in/agn-kosiak),
[Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/),
[Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/).
Members of the ACK Cyfronet AGH team providing valuable support and expertise:
[Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/),
[Marek Magryล](https://www.linkedin.com/in/magrys/),
[Mieszko Cholewa ](https://www.linkedin.com/in/mieszko-cholewa-613726301/).
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/pv4brQMDTy).
|
PrunaAI/SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed | PrunaAI | 2024-11-13T13:07:58Z | 12 | 2 | pruna-engine | [
"pruna-engine",
"license:apache-2.0",
"region:us"
] | null | 2024-02-12T13:24:46Z | ---
license: apache-2.0
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.
- ***How does the model quality change?*** The quality of the model output might slightly vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
## Setup
You can run the smashed model with these steps:
0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
3. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed --local-dir SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "SG161222-Realistic_Vision_V1.4-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of.
```
## Configurations
The configuration info are in `config.json`.
## Credits & License
We follow the same license as the original model. Please check the license of the original model SG161222/Realistic_Vision_V1.4 before using this model which provided the base model.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
cuongdev/7nguoi-7000 | cuongdev | 2024-11-13T12:58:39Z | 32 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-11-13T12:54:52Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 7nguoi-7000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
el3oss/Llama-3-8B-Instruct-defect-fix2 | el3oss | 2024-11-13T12:53:45Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T12:49:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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deepnet/SN29-C00-llama-HK2Nw-1 | deepnet | 2024-11-13T12:53:01Z | 36 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T12:30:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **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]
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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ISAdoraym/dreambooth-sd3-lora1 | ISAdoraym | 2024-11-13T12:52:24Z | 5 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"sd3",
"sd3-diffusers",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-3-medium-diffusers",
"base_model:adapter:stabilityai/stable-diffusion-3-medium-diffusers",
"license:openrail++",
"region:us"
] | text-to-image | 2024-11-13T06:41:09Z | ---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- sd3
- sd3-diffusers
- template:sd-lora
instance_prompt: a photo of cool shirt
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SD3 DreamBooth LoRA - ISAdoraym/dreambooth-sd3-lora1
<Gallery />
## Model description
These are ISAdoraym/dreambooth-sd3-lora1 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `a photo of cool shirt` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](ISAdoraym/dreambooth-sd3-lora1/tree/main) in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-3-medium-diffusers', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ISAdoraym/dreambooth-sd3-lora1', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('A photo of cool shirt').images[0]
```
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`diffusers_lora_weights.safetensors` here ๐พ](/ISAdoraym/dreambooth-sd3-lora1/blob/main/diffusers_lora_weights.safetensors)**.
- Rename it and place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
BlinkDL/rwkv-6-world | BlinkDL | 2024-11-13T12:51:56Z | 0 | 144 | null | [
"pytorch",
"text-generation",
"causal-lm",
"rwkv",
"en",
"zh",
"fr",
"es",
"de",
"pt",
"ru",
"it",
"ja",
"ko",
"vi",
"ar",
"dataset:cerebras/SlimPajama-627B",
"dataset:EleutherAI/pile",
"dataset:bigcode/starcoderdata",
"dataset:oscar-corpus/OSCAR-2301",
"arxiv:2404.05892",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-02-08T17:47:54Z | ---
language:
- en
- zh
- fr
- es
- de
- pt
- ru
- it
- ja
- ko
- vi
- ar
tags:
- pytorch
- text-generation
- causal-lm
- rwkv
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- EleutherAI/pile
- bigcode/starcoderdata
- oscar-corpus/OSCAR-2301
---
# RWKV-6 World
RWKV-6 paper: https://arxiv.org/abs/2404.05892
Use rwkv pip package 0.8.24+ for RWKV-6 inference: https://pypi.org/project/rwkv/ (pipeline = PIPELINE(model, "rwkv_vocab_v20230424") for rwkv-world models)
Online Demo 1: https://huggingface.co/spaces/BlinkDL/RWKV-Gradio-2
Online Demo 2: https://huggingface.co/spaces/BlinkDL/RWKV-Gradio-1
GUI: https://github.com/josStorer/RWKV-Runner (see Releases)
For developer: https://github.com/BlinkDL/ChatRWKV/blob/main/API_DEMO_CHAT.py
https://github.com/BlinkDL/ChatRWKV/blob/main/RWKV_v6_demo.py
https://www.rwkv.com/
RWKV-6 7B v3 MMLU = 54.2% (using the same "47.9%" code)
RWKV-6 7B v2.1 MMLU = 47.9%: https://github.com/Jellyfish042/rwkv_mmlu
RWKV-6 0.1B (using pythia-160m tokenizer): https://huggingface.co/BlinkDL/temp-latest-training-models/blob/main/temp/rwkv-x060-173m-pile-20240515-ctx4k.pth
## Model Description
RWKV-6 trained on 100+ world languages (70% English, 15% multilang, 15% code).
World = Some_Pile + Some_SlimPajama + Some_StarCoder + Some_OSCAR + All_Wikipedia + All_ChatGPT_Data_I_can_find
World v1 = 0.59T tokens
World v2 = 1.12T tokens
World v2.1 = 1.42T tokens
Recommended fine-tuning format (use \n for newlines):
```
User: xxxxxxxxxxxxxxx
Assistant: xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
User: xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
Assistant: xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
xxxxxxxxxxxxxxx
```
A good chat prompt (better replace \n\n in xxx to \n, such that there will be no newlines in xxx):
```
User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: xxx
Assistant:
```
QA prompt (better replace \n\n in xxx to \n, such that there will be no newlines in xxx):
```
Question: xxx
Answer:
```
and
```
Instruction: xxx
Input: xxx
Response:
```
!!! There should not be any space after your final ":" or you will upset the tokenizer and see non-English reponse !!!
!!! There should not be any space after your final ":" or you will upset the tokenizer and see non-English reponse !!!
!!! There should not be any space after your final ":" or you will upset the tokenizer and see non-English reponse !!!
|
personal1802/ntrMIXIllustriousXL_v21 | personal1802 | 2024-11-13T12:50:47Z | 11 | 1 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:Raelina/Raehoshi-illust-XL",
"base_model:adapter:Raelina/Raehoshi-illust-XL",
"region:us"
] | text-to-image | 2024-11-13T12:40:08Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/WHITE.png
base_model: Raelina/Raehoshi-illust-XL
instance_prompt: null
---
# ntrMIXIllustriousXL_v21
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/personal1802/ntrMIXIllustriousXL_v21/tree/main) them in the Files & versions tab.
|
mikasenghaas/gpt2-xl-fresh | mikasenghaas | 2024-11-13T12:43:25Z | 6 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2024-11-13T12:39:06Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
mikasenghaas/gpt2-large-fresh | mikasenghaas | 2024-11-13T12:38:34Z | 12 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2024-11-13T12:37:30Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
khilan-crest/twitter-roberta-base-sentiment-latest_13112024T162211 | khilan-crest | 2024-11-13T12:33:25Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment-latest",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment-latest",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-11-13T12:31:50Z | ---
library_name: transformers
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: twitter-roberta-base-sentiment-latest_13112024T162211
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. -->
# twitter-roberta-base-sentiment-latest_13112024T162211
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2174
- F1: 0.6307
- Learning Rate: 0.0
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Rate |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log | 1.0 | 315 | 1.0491 | 0.5960 | 0.0000 |
| 1.2342 | 2.0 | 630 | 0.9992 | 0.6537 | 0.0000 |
| 1.2342 | 3.0 | 945 | 1.1168 | 0.6244 | 0.0000 |
| 0.7754 | 4.0 | 1260 | 1.1775 | 0.6337 | 0.0000 |
| 0.5224 | 5.0 | 1575 | 1.2174 | 0.6307 | 0.0 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MarsupialAI/UnslopNemo-12B-v3_EXL2_6bpw_H8 | MarsupialAI | 2024-11-13T12:32:00Z | 8 | 1 | null | [
"safetensors",
"mistral",
"6-bit",
"exl2",
"region:us"
] | null | 2024-11-13T12:24:49Z | 6.0bpw EXL2 quant of https://huggingface.co/TheDrummer/UnslopNemo-12B-v3
8bit heads. Default measurement dataset. |
prostponer/assmann | prostponer | 2024-11-13T12:24:38Z | 6 | 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 | 2024-11-13T11:48:17Z | ---
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: assmann
---
# Assmann
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `assmann` to trigger the image generation.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('prostponer/assmann', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
AhmaadAwais/opt-125m-gptq | AhmaadAwais | 2024-11-13T12:20:19Z | 82 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-11-13T12:20:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nguyenphuthien/flan-t5-large-Q4_K_M-GGUF | nguyenphuthien | 2024-11-13T12:19:20Z | 5 | 0 | null | [
"gguf",
"text2text-generation",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"ro",
"de",
"multilingual",
"dataset:svakulenk0/qrecc",
"dataset:taskmaster2",
"dataset:djaym7/wiki_dialog",
"dataset:deepmind/code_contests",
"dataset:lambada",
"dataset:gsm8k",
"dataset:aqua_rat",
"dataset:esnli",
"dataset:quasc",
"dataset:qed",
"base_model:google/flan-t5-large",
"base_model:quantized:google/flan-t5-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-11-13T12:19:16Z | ---
language:
- en
- fr
- ro
- de
- multilingual
widget:
- text: 'Translate to German: My name is Arthur'
example_title: Translation
- text: Please answer to the following question. Who is going to be the next Ballon
d'or?
example_title: Question Answering
- text: 'Q: Can Geoffrey Hinton have a conversation with George Washington? Give the
rationale before answering.'
example_title: Logical reasoning
- text: Please answer the following question. What is the boiling point of Nitrogen?
example_title: Scientific knowledge
- text: Answer the following yes/no question. Can you write a whole Haiku in a single
tweet?
example_title: Yes/no question
- text: Answer the following yes/no question by reasoning step-by-step. Can you write
a whole Haiku in a single tweet?
example_title: Reasoning task
- text: 'Q: ( False or not False or False ) is? A: Let''s think step by step'
example_title: Boolean Expressions
- text: The square root of x is the cube root of y. What is y to the power of 2, if
x = 4?
example_title: Math reasoning
- text: 'Premise: At my age you will probably have learnt one lesson. Hypothesis: It''s
not certain how many lessons you''ll learn by your thirties. Does the premise
entail the hypothesis?'
example_title: Premise and hypothesis
tags:
- text2text-generation
- llama-cpp
- gguf-my-repo
datasets:
- svakulenk0/qrecc
- taskmaster2
- djaym7/wiki_dialog
- deepmind/code_contests
- lambada
- gsm8k
- aqua_rat
- esnli
- quasc
- qed
license: apache-2.0
base_model: google/flan-t5-large
---
# nguyenphuthien/flan-t5-large-Q4_K_M-GGUF
This model was converted to GGUF format from [`google/flan-t5-large`](https://huggingface.co/google/flan-t5-large) 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/google/flan-t5-large) 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 nguyenphuthien/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo nguyenphuthien/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.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 nguyenphuthien/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo nguyenphuthien/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -c 2048
```
|
deepfile/multilingual-e5-small-onnx-qint8 | deepfile | 2024-11-13T12:17:58Z | 38 | 1 | sentence-transformers | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-11-13T10:46:03Z | ---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: multilingual-e5-small
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.22266660528253
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.79980849482483
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.747820820329736
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.045143830596146
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
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dataset:
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metrics:
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dataset:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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metrics:
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- task:
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dataset:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
type: Classification
dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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- task:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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dataset:
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config: te
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 62.111634162743776
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dataset:
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config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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dataset:
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config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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- task:
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dataset:
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config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
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value: 68.31876260928043
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type: Clustering
dataset:
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split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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dataset:
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config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 27.259158476693774
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dataset:
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split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
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value: 31.15758529581164
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split: test
revision: None
metrics:
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value: 11.565
- type: map_at_100
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value: 8.749
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value: 9.974
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value: 42.105
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value: 50.589
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value: 51.187000000000005
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value: 51.233
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value: 48.246
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value: 40.402
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- type: ndcg_at_1000
value: 36.905
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value: 35.983
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value: 33.764
- type: precision_at_1
value: 42.105
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value: 22.786
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value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
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value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
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value: 9.792
- type: recall_at_5
value: 11.882
- task:
type: Retrieval
dataset:
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name: MTEB NQ
config: default
split: test
revision: None
metrics:
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value: 33.852
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value: 48.924
- type: map_at_100
value: 49.854
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value: 49.886
- type: map_at_3
value: 44.9
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value: 47.387
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value: 38.035999999999994
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value: 51.644
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value: 52.339
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value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
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value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
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value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
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value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 39.13064340585255
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 58.97884249325877
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.232731157702425
- type: cos_sim_spearman
value: 59.89531877658345
- type: euclidean_pearson
value: 49.937914570348376
- type: euclidean_spearman
value: 60.220905659334036
- type: manhattan_pearson
value: 50.00987996844193
- type: manhattan_spearman
value: 60.081341480977926
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.52464042600003
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.071631948736
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 62.156507936507936
- type: precision
value: 59.84964285714286
- type: recall
value: 68.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.52126366950182
- type: f1
value: 72.8496210148701
- type: precision
value: 70.92171498003819
- type: recall
value: 77.52126366950182
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.78260869565217
- type: f1
value: 65.32422360248447
- type: precision
value: 63.063067367415194
- type: recall
value: 70.78260869565217
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.43478260869566
- type: f1
value: 73.02608695652172
- type: precision
value: 70.63768115942028
- type: recall
value: 78.43478260869566
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.9
- type: f1
value: 55.309753694581275
- type: precision
value: 53.130476190476195
- type: recall
value: 60.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.89999999999999
- type: f1
value: 67.92023809523809
- type: precision
value: 65.82595238095237
- type: recall
value: 72.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.80337756332931
- type: f1
value: 39.42174900558496
- type: precision
value: 36.97101116280851
- type: recall
value: 46.80337756332931
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
- type: recall
value: 89.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.199999999999996
- type: f1
value: 39.95484348984349
- type: precision
value: 37.561071428571424
- type: recall
value: 47.199999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.8
- type: f1
value: 84.68190476190475
- type: precision
value: 83.275
- type: recall
value: 87.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.76190476190476
- type: f1
value: 42.14965986394558
- type: precision
value: 39.96743626743626
- type: recall
value: 48.76190476190476
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.10000000000001
- type: f1
value: 59.58580086580086
- type: precision
value: 57.150238095238095
- type: recall
value: 66.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.3
- type: f1
value: 84.0
- type: precision
value: 82.48666666666666
- type: recall
value: 87.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 87.79523809523809
- type: precision
value: 86.6
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.0
- type: f1
value: 83.81
- type: precision
value: 82.36666666666666
- type: recall
value: 87.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.9
- type: f1
value: 57.76533189033189
- type: precision
value: 55.50595238095239
- type: recall
value: 63.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.1
- type: f1
value: 71.83690476190478
- type: precision
value: 70.04928571428573
- type: recall
value: 76.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.3
- type: f1
value: 59.32626984126984
- type: precision
value: 56.62535714285713
- type: recall
value: 66.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.60000000000001
- type: f1
value: 87.96333333333334
- type: precision
value: 86.73333333333333
- type: recall
value: 90.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.16666666666666
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.71428571428571
- type: f1
value: 82.29142600436403
- type: precision
value: 80.8076626877166
- type: recall
value: 85.71428571428571
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.88888888888889
- type: f1
value: 85.7834757834758
- type: precision
value: 84.43732193732193
- type: recall
value: 88.88888888888889
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 85.67190476190476
- type: precision
value: 84.43333333333332
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.72727272727273
- type: f1
value: 78.21969696969695
- type: precision
value: 76.18181818181819
- type: recall
value: 82.72727272727273
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.0062893081761
- type: f1
value: 55.13976240391334
- type: precision
value: 52.92112499659669
- type: recall
value: 61.0062893081761
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.86666666666666
- type: precision
value: 85.69166666666668
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.54085603112841
- type: f1
value: 68.56031128404669
- type: precision
value: 66.53047989623866
- type: recall
value: 73.54085603112841
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.58974358974359
- type: f1
value: 36.45299145299145
- type: precision
value: 33.81155881155882
- type: recall
value: 43.58974358974359
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.599999999999994
- type: f1
value: 53.264689754689755
- type: precision
value: 50.869166666666665
- type: recall
value: 59.599999999999994
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.2
- type: f1
value: 81.61666666666665
- type: precision
value: 80.02833333333335
- type: recall
value: 85.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.78504672897196
- type: f1
value: 58.00029669188548
- type: precision
value: 55.815809968847354
- type: recall
value: 63.78504672897196
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.5
- type: f1
value: 61.518333333333345
- type: precision
value: 59.622363699102834
- type: recall
value: 66.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 85.60222222222221
- type: precision
value: 84.27916666666665
- type: recall
value: 88.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.699999999999996
- type: f1
value: 52.732375957375965
- type: precision
value: 50.63214035964035
- type: recall
value: 58.699999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.99666666666667
- type: precision
value: 89.03333333333333
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.10000000000001
- type: f1
value: 87.55666666666667
- type: precision
value: 86.36166666666668
- type: recall
value: 90.10000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 88.89000000000001
- type: precision
value: 87.71166666666666
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.7
- type: f1
value: 60.67427750410509
- type: precision
value: 58.71785714285714
- type: recall
value: 65.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 81.93190476190475
- type: precision
value: 80.37833333333333
- type: recall
value: 85.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.833333333333336
- type: f1
value: 42.006625781625786
- type: precision
value: 40.077380952380956
- type: recall
value: 47.833333333333336
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.4
- type: f1
value: 8.24465007215007
- type: precision
value: 7.664597069597071
- type: recall
value: 10.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.6
- type: f1
value: 77.76333333333334
- type: precision
value: 75.57833333333332
- type: recall
value: 82.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.67857142857143
- type: f1
value: 44.302721088435376
- type: precision
value: 41.49801587301587
- type: recall
value: 52.67857142857143
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.3205268935236
- type: f1
value: 22.426666605171157
- type: precision
value: 20.685900116470915
- type: recall
value: 28.3205268935236
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22.7
- type: f1
value: 17.833970473970474
- type: precision
value: 16.407335164835164
- type: recall
value: 22.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 89.92999999999999
- type: precision
value: 88.87
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.25
- type: precision
value: 88.21666666666667
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.19999999999999
- type: f1
value: 63.38269841269841
- type: precision
value: 61.14773809523809
- type: recall
value: 69.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.8
- type: f1
value: 42.839915639915645
- type: precision
value: 40.770287114845935
- type: recall
value: 48.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pol-eng)
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.8
- type: f1
value: 85.90666666666668
- type: precision
value: 84.54166666666666
- type: recall
value: 88.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (war-eng)
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.6
- type: f1
value: 40.85892920804686
- type: precision
value: 38.838223114604695
- type: recall
value: 46.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (aze-eng)
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.0
- type: f1
value: 80.14190476190475
- type: precision
value: 78.45333333333333
- type: recall
value: 84.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (vie-eng)
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5
- type: f1
value: 87.78333333333333
- type: precision
value: 86.5
- type: recall
value: 90.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nno-eng)
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.5
- type: f1
value: 69.48397546897547
- type: precision
value: 67.51869047619049
- type: recall
value: 74.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cha-eng)
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 32.846715328467155
- type: f1
value: 27.828177499710343
- type: precision
value: 26.63451511991658
- type: recall
value: 32.846715328467155
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mhr-eng)
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.0
- type: f1
value: 6.07664116764988
- type: precision
value: 5.544177607179943
- type: recall
value: 8.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dan-eng)
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.38555555555554
- type: precision
value: 82.91583333333334
- type: recall
value: 87.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 84.08333333333331
- type: precision
value: 82.47333333333333
- type: recall
value: 87.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (amh-eng)
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.95238095238095
- type: f1
value: 76.13095238095238
- type: precision
value: 74.05753968253967
- type: recall
value: 80.95238095238095
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pam-eng)
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.799999999999999
- type: f1
value: 6.971422975172975
- type: precision
value: 6.557814916172301
- type: recall
value: 8.799999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hsb-eng)
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.099378881987576
- type: f1
value: 37.01649742022413
- type: precision
value: 34.69420618488942
- type: recall
value: 44.099378881987576
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.3
- type: f1
value: 80.32666666666667
- type: precision
value: 78.60666666666665
- type: recall
value: 84.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.5
- type: f1
value: 90.49666666666666
- type: precision
value: 89.56666666666668
- type: recall
value: 92.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.0
- type: f1
value: 8.268423529875141
- type: precision
value: 7.878118605532398
- type: recall
value: 10.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.22077922077922
- type: f1
value: 74.27128427128426
- type: precision
value: 72.28715728715729
- type: recall
value: 79.22077922077922
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.64885496183206
- type: f1
value: 58.87495456197747
- type: precision
value: 55.992366412213734
- type: recall
value: 65.64885496183206
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.06986899563319
- type: f1
value: 94.78408539543909
- type: precision
value: 94.15332362930616
- type: recall
value: 96.06986899563319
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.2
- type: f1
value: 71.72571428571428
- type: precision
value: 69.41000000000001
- type: recall
value: 77.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.4406779661017
- type: f1
value: 83.2391713747646
- type: precision
value: 81.74199623352166
- type: recall
value: 86.4406779661017
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.4
- type: f1
value: 6.017828743398003
- type: precision
value: 5.4829865484756795
- type: recall
value: 8.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.5
- type: f1
value: 79.74833333333333
- type: precision
value: 78.04837662337664
- type: recall
value: 83.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.4
- type: f1
value: 54.467301587301584
- type: precision
value: 52.23242424242424
- type: recall
value: 60.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.9
- type: f1
value: 69.68699134199134
- type: precision
value: 67.59873015873016
- type: recall
value: 74.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.9652380952381
- type: precision
value: 83.66166666666666
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.1
- type: f1
value: 7.681244588744588
- type: precision
value: 7.370043290043291
- type: recall
value: 9.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.9651474530831
- type: f1
value: 76.84220605132133
- type: precision
value: 75.19606398962966
- type: recall
value: 80.9651474530831
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jpn-eng)
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.705
- type: precision
value: 82.3120634920635
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (csb-eng)
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 23.98763072676116
- type: precision
value: 22.506399397703746
- type: recall
value: 29.64426877470356
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.4225352112676
- type: f1
value: 62.84037558685445
- type: precision
value: 59.56572769953053
- type: recall
value: 70.4225352112676
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 19.64071856287425
- type: f1
value: 15.125271011207756
- type: precision
value: 13.865019261197494
- type: recall
value: 19.64071856287425
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ind-eng)
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.80666666666666
- type: precision
value: 86.70833333333331
- type: recall
value: 90.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tuk-eng)
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 18.407224958949097
- type: precision
value: 16.982385430661292
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.98591549295775
- type: f1
value: 49.94718309859154
- type: precision
value: 47.77864154624717
- type: recall
value: 55.98591549295775
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swh-eng)
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.07692307692307
- type: f1
value: 66.74358974358974
- type: precision
value: 64.06837606837607
- type: recall
value: 73.07692307692307
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.25
- type: precision
value: 92.43333333333332
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.78705636743215
- type: f1
value: 31.63899658680452
- type: precision
value: 29.72264397629742
- type: recall
value: 37.78705636743215
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.6
- type: f1
value: 16.91697302697303
- type: precision
value: 15.71225147075147
- type: recall
value: 21.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.01628664495115
- type: f1
value: 81.38514037536838
- type: precision
value: 79.83170466883823
- type: recall
value: 85.01628664495115
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.39999999999999
- type: f1
value: 79.96380952380952
- type: precision
value: 78.48333333333333
- type: recall
value: 83.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
- type: precision
value: 77.58833333333334
- type: recall
value: 83.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.59055118110236
- type: f1
value: 71.66854143232096
- type: precision
value: 70.30183727034121
- type: recall
value: 75.59055118110236
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.5
- type: f1
value: 59.26095238095238
- type: precision
value: 56.81909090909092
- type: recall
value: 65.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.26315789473685
- type: f1
value: 47.986523325858506
- type: precision
value: 45.33950006595436
- type: recall
value: 55.26315789473685
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.89999999999999
- type: f1
value: 78.835
- type: precision
value: 77.04761904761905
- type: recall
value: 82.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
- type: precision
value: 33.57371794871795
- type: recall
value: 43.269230769230774
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
value: 14.133999999999999
- type: map_at_1000
value: 15.668000000000001
- type: map_at_3
value: 5.862
- type: map_at_5
value: 7.17
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 42.931000000000004
- type: mrr_at_100
value: 44.81
- type: mrr_at_1000
value: 44.81
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.701
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 21.163
- type: ndcg_at_100
value: 33.306000000000004
- type: ndcg_at_1000
value: 45.275999999999996
- type: ndcg_at_3
value: 25.685999999999996
- type: ndcg_at_5
value: 23.732
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_100
value: 6.938999999999999
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.896
- type: recall_at_10
value: 13.333999999999998
- type: recall_at_100
value: 43.517
- type: recall_at_1000
value: 79.836
- type: recall_at_3
value: 6.306000000000001
- type: recall_at_5
value: 8.825
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.3874
- type: ap
value: 13.829909072469423
- type: f1
value: 53.54534203543492
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 33.21527881409797
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.97943613280086
- type: cos_sim_ap
value: 70.75454316885921
- type: cos_sim_f1
value: 65.38274012676743
- type: cos_sim_precision
value: 60.761214318078835
- type: cos_sim_recall
value: 70.76517150395777
- type: dot_accuracy
value: 79.0546581629612
- type: dot_ap
value: 47.3197121792147
- type: dot_f1
value: 49.20106524633821
- type: dot_precision
value: 42.45499808502489
- type: dot_recall
value: 58.49604221635884
- type: euclidean_accuracy
value: 85.08076533349228
- type: euclidean_ap
value: 70.95016106374474
- type: euclidean_f1
value: 65.43987900176455
- type: euclidean_precision
value: 62.64478764478765
- type: euclidean_recall
value: 68.49604221635884
- type: manhattan_accuracy
value: 84.93771234428085
- type: manhattan_ap
value: 70.63668388755362
- type: manhattan_f1
value: 65.23895401262398
- type: manhattan_precision
value: 56.946084218811485
- type: manhattan_recall
value: 76.35883905013192
- type: max_accuracy
value: 85.08076533349228
- type: max_ap
value: 70.95016106374474
- type: max_f1
value: 65.43987900176455
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.69096130709822
- type: cos_sim_ap
value: 84.82526278228542
- type: cos_sim_f1
value: 77.65485060585536
- type: cos_sim_precision
value: 75.94582658619167
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 80.97954748321496
- type: dot_ap
value: 64.81642914145866
- type: dot_f1
value: 60.631996987229975
- type: dot_precision
value: 54.5897293631712
- type: dot_recall
value: 68.17831844779796
- type: euclidean_accuracy
value: 88.6987231730508
- type: euclidean_ap
value: 84.80003825477253
- type: euclidean_f1
value: 77.67194179854496
- type: euclidean_precision
value: 75.7128235122094
- type: euclidean_recall
value: 79.73514012935017
- type: manhattan_accuracy
value: 88.62692591298949
- type: manhattan_ap
value: 84.80451408255276
- type: manhattan_f1
value: 77.69888949572183
- type: manhattan_precision
value: 73.70311528631622
- type: manhattan_recall
value: 82.15275639051433
- type: max_accuracy
value: 88.6987231730508
- type: max_ap
value: 84.82526278228542
- type: max_f1
value: 77.69888949572183
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
---
### Optimized and quantized of the original model
Optimization format: `ONNX`
Quantization: `int8`
Original model is available at [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) |
ihughes15234/llama_3_1_8bi_tictactoe_dpo5epoch_v3 | ihughes15234 | 2024-11-13T12:16:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:ihughes15234/llama_3_1_8bi_tictactoe_dpo3epoch_v3",
"base_model:finetune:ihughes15234/llama_3_1_8bi_tictactoe_dpo3epoch_v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T12:10:18Z | ---
base_model: ihughes15234/llama_3_1_8bi_tictactoe_dpo3epoch_v3
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ihughes15234
- **License:** apache-2.0
- **Finetuned from model :** ihughes15234/llama_3_1_8bi_tictactoe_dpo3epoch_v3
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bibibobo777/Hw4_model | bibibobo777 | 2024-11-13T12:11:26Z | 9 | 0 | null | [
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"region:us"
] | null | 2024-11-12T04:10:32Z | ---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: Hw4_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. -->
# Hw4_model
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3143
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 471 | 0.3513 |
| 0.4173 | 2.0 | 942 | 0.3171 |
| 0.3049 | 3.0 | 1413 | 0.3143 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
lyhourt/distilbert-finetuned-emotion | lyhourt | 2024-11-13T12:10:10Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-11-11T05:08:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aalof/seq2seq_imlla | aalof | 2024-11-13T12:06:47Z | 49 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"custom_seq2seq",
"generated_from_trainer",
"dataset:iva_mt_wslot",
"endpoints_compatible",
"region:us"
] | null | 2024-11-13T12:06:37Z | ---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- iva_mt_wslot
metrics:
- bleu
model-index:
- name: seq2seq_imlla
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. -->
# seq2seq_imlla
This model is a fine-tuned version of [](https://huggingface.co/) on the iva_mt_wslot dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0658
- Bleu: 0.0042
- Gen Len: 5.8248
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:-------:|
| 7.441 | 0.9992 | 636 | 7.0735 | 0.0 | 5.3206 |
| 6.5312 | 2.0 | 1273 | 6.4544 | 0.0175 | 9.5238 |
| 6.0704 | 2.9992 | 1909 | 6.2110 | 0.0007 | 4.9967 |
| 5.8907 | 4.0 | 2546 | 6.1000 | 0.0055 | 6.944 |
| 5.7606 | 4.9961 | 3180 | 6.0658 | 0.0042 | 5.8248 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
sweetpapa/test1 | sweetpapa | 2024-11-13T11:49:43Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T11:45:33Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/internlm2-chat-20b-sft-GGUF | mradermacher | 2024-11-13T11:48:59Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:internlm/internlm2-chat-20b-sft",
"base_model:quantized:internlm/internlm2-chat-20b-sft",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-12T07:22:10Z | ---
base_model: internlm/internlm2-chat-20b-sft
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/internlm/internlm2-chat-20b-sft
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/internlm2-chat-20b-sft-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/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q2_K.gguf) | Q2_K | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q3_K_S.gguf) | Q3_K_S | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q3_K_M.gguf) | Q3_K_M | 9.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q3_K_L.gguf) | Q3_K_L | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.IQ4_XS.gguf) | IQ4_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q4_K_S.gguf) | Q4_K_S | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q4_K_M.gguf) | Q4_K_M | 12.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q5_K_S.gguf) | Q5_K_S | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q5_K_M.gguf) | Q5_K_M | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q6_K.gguf) | Q6_K | 16.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/internlm2-chat-20b-sft-GGUF/resolve/main/internlm2-chat-20b-sft.Q8_0.gguf) | Q8_0 | 21.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
RichardErkhov/Kaspar_-_QueerGPT2-gguf | RichardErkhov | 2024-11-13T11:48:59Z | 57 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-11-13T11:37:53Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
QueerGPT2 - GGUF
- Model creator: https://huggingface.co/Kaspar/
- Original model: https://huggingface.co/Kaspar/QueerGPT2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [QueerGPT2.Q2_K.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q2_K.gguf) | Q2_K | 0.08GB |
| [QueerGPT2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q3_K_S.gguf) | Q3_K_S | 0.08GB |
| [QueerGPT2.Q3_K.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q3_K.gguf) | Q3_K | 0.09GB |
| [QueerGPT2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q3_K_M.gguf) | Q3_K_M | 0.09GB |
| [QueerGPT2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q3_K_L.gguf) | Q3_K_L | 0.1GB |
| [QueerGPT2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.IQ4_XS.gguf) | IQ4_XS | 0.1GB |
| [QueerGPT2.Q4_0.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q4_0.gguf) | Q4_0 | 0.1GB |
| [QueerGPT2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.IQ4_NL.gguf) | IQ4_NL | 0.1GB |
| [QueerGPT2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q4_K_S.gguf) | Q4_K_S | 0.1GB |
| [QueerGPT2.Q4_K.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q4_K.gguf) | Q4_K | 0.11GB |
| [QueerGPT2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q4_K_M.gguf) | Q4_K_M | 0.11GB |
| [QueerGPT2.Q4_1.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q4_1.gguf) | Q4_1 | 0.11GB |
| [QueerGPT2.Q5_0.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q5_0.gguf) | Q5_0 | 0.11GB |
| [QueerGPT2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q5_K_S.gguf) | Q5_K_S | 0.11GB |
| [QueerGPT2.Q5_K.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q5_K.gguf) | Q5_K | 0.12GB |
| [QueerGPT2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q5_K_M.gguf) | Q5_K_M | 0.12GB |
| [QueerGPT2.Q5_1.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q5_1.gguf) | Q5_1 | 0.12GB |
| [QueerGPT2.Q6_K.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q6_K.gguf) | Q6_K | 0.13GB |
| [QueerGPT2.Q8_0.gguf](https://huggingface.co/RichardErkhov/Kaspar_-_QueerGPT2-gguf/blob/main/QueerGPT2.Q8_0.gguf) | Q8_0 | 0.17GB |
Original model description:
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: QueerGPT2
results: []
widget:
- text: "When I grow up, I want to be a"
---
<!-- 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. -->
# QueerGPT2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3634
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.5433 | 1.0 | 13237 | 4.3634 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
mradermacher/bigstral-12b-32k-8xMoE-GGUF | mradermacher | 2024-11-13T11:36:50Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:bartowski/bigstral-12b-32k-8xMoE",
"base_model:quantized:bartowski/bigstral-12b-32k-8xMoE",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-13T07:24:38Z | ---
base_model: bartowski/bigstral-12b-32k-8xMoE
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/bartowski/bigstral-12b-32k-8xMoE
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-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/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q2_K.gguf) | Q2_K | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q3_K_S.gguf) | Q3_K_S | 35.7 | |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q3_K_M.gguf) | Q3_K_M | 39.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q3_K_L.gguf) | Q3_K_L | 42.3 | |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.IQ4_XS.gguf) | IQ4_XS | 44.4 | |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q4_K_S.gguf) | Q4_K_S | 46.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q4_K_M.gguf) | Q4_K_M | 49.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q5_K_S.gguf.part2of2) | Q5_K_S | 56.4 | |
| [PART 1](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q5_K_M.gguf.part2of2) | Q5_K_M | 58.1 | |
| [PART 1](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q6_K.gguf.part2of2) | Q6_K | 67.1 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/bigstral-12b-32k-8xMoE-GGUF/resolve/main/bigstral-12b-32k-8xMoE.Q8_0.gguf.part2of2) | Q8_0 | 86.7 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Rohan-G/bnb_nf4_quantization | Rohan-G | 2024-11-13T11:26:30Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-11-13T11:21:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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vijay-ravichander/Llama-1B-Summarization-LoRA-MLP-r64-merged | vijay-ravichander | 2024-11-13T11:22:24Z | 84 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T11:19:33Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Pandistellina/merged-model-sentiment-llama3 | Pandistellina | 2024-11-13T11:18:57Z | 119 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T11:13:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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haris-waqar/TrimLesson6 | haris-waqar | 2024-11-13T11:17:28Z | 164 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-11-13T10:03:34Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: TrimLesson6
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. -->
# TrimLesson6
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2638
- Accuracy: 0.9020
- F1-score: 0.8990
- Recall-score: 0.9020
- Precision-score: 0.9085
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- 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: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Recall-score | Precision-score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:------------:|:---------------:|
| 3.0068 | 1.0 | 427 | 3.0157 | 0.2007 | 0.1021 | 0.2007 | 0.1514 |
| 2.0798 | 2.0 | 854 | 1.8506 | 0.5478 | 0.4726 | 0.5478 | 0.4815 |
| 1.5096 | 3.0 | 1281 | 1.1647 | 0.6963 | 0.6441 | 0.6963 | 0.6785 |
| 0.8799 | 4.0 | 1708 | 0.8187 | 0.7240 | 0.6793 | 0.7240 | 0.7035 |
| 1.8621 | 5.0 | 2135 | 0.7214 | 0.7409 | 0.7049 | 0.7409 | 0.7304 |
| 0.9176 | 6.0 | 2562 | 0.6481 | 0.7564 | 0.7249 | 0.7564 | 0.7461 |
| 0.7314 | 7.0 | 2989 | 0.5848 | 0.7695 | 0.7321 | 0.7695 | 0.7379 |
| 0.2837 | 8.0 | 3416 | 0.5256 | 0.7858 | 0.7592 | 0.7858 | 0.7798 |
| 0.5412 | 9.0 | 3843 | 0.5331 | 0.7852 | 0.7561 | 0.7852 | 0.7785 |
| 0.545 | 10.0 | 4270 | 0.5223 | 0.7893 | 0.7590 | 0.7893 | 0.7974 |
| 0.6444 | 11.0 | 4697 | 0.4780 | 0.8057 | 0.7896 | 0.8057 | 0.7977 |
| 0.6496 | 12.0 | 5124 | 0.4717 | 0.8049 | 0.7771 | 0.8049 | 0.8083 |
| 0.1724 | 13.0 | 5551 | 0.4521 | 0.8188 | 0.7994 | 0.8188 | 0.8357 |
| 0.4841 | 14.0 | 5978 | 0.4289 | 0.8226 | 0.8109 | 0.8226 | 0.8309 |
| 0.3883 | 15.0 | 6405 | 0.4123 | 0.8268 | 0.8075 | 0.8268 | 0.8255 |
| 0.6509 | 16.0 | 6832 | 0.3927 | 0.8467 | 0.8400 | 0.8467 | 0.8559 |
| 0.6592 | 17.0 | 7259 | 0.3711 | 0.8503 | 0.8415 | 0.8503 | 0.8617 |
| 0.2939 | 18.0 | 7686 | 0.3645 | 0.8525 | 0.8368 | 0.8525 | 0.8687 |
| 0.0568 | 19.0 | 8113 | 0.3307 | 0.8727 | 0.8675 | 0.8727 | 0.8806 |
| 0.2942 | 20.0 | 8540 | 0.3354 | 0.8715 | 0.8668 | 0.8715 | 0.8800 |
| 0.4429 | 21.0 | 8967 | 0.3063 | 0.8821 | 0.8775 | 0.8821 | 0.8892 |
| 0.1764 | 22.0 | 9394 | 0.2903 | 0.8904 | 0.8849 | 0.8904 | 0.9002 |
| 0.0734 | 23.0 | 9821 | 0.2816 | 0.8927 | 0.8873 | 0.8927 | 0.9007 |
| 0.5793 | 24.0 | 10248 | 0.2635 | 0.9077 | 0.9062 | 0.9077 | 0.9092 |
| 0.2896 | 25.0 | 10675 | 0.2638 | 0.9020 | 0.8990 | 0.9020 | 0.9085 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 2.20.0
- Tokenizers 0.20.0
|
Zekunli/qwen2.5-7b-alpaca-dsg | Zekunli | 2024-11-13T11:15:55Z | 37 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T11:07:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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. -->
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### Training Procedure
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#### 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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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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## Technical Specifications [optional]
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TucanoBR/XGBRegressor-text-filter | TucanoBR | 2024-11-13T11:13:34Z | 0 | 0 | xgboost | [
"xgboost",
"text-quality",
"portuguese",
"pt",
"dataset:TucanoBR/GigaVerbo-Text-Filter",
"arxiv:2411.07854",
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T15:44:12Z | ---
license: apache-2.0
datasets:
- TucanoBR/GigaVerbo-Text-Filter
language:
- pt
metrics:
- mse
library_name: xgboost
tags:
- text-quality
- portuguese
---
# XGBRegressor-text-filter
XGBRegressor-text-filter is a text-quality filter built on top of the [`xgboost`](https://xgboost.readthedocs.io/en/stable/) library. It uses the embeddings generated by [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) as a feature vector.
This repository has the [source code](https://github.com/Nkluge-correa/Tucano) used to train this model.
## Usage
Here's an example of how to use the XGBRegressor-text-filter:
```python
from transformers import AutoTokenizer, AutoModel
from xgboost import XGBRegressor
import torch.nn.functional as F
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
embedding_model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
device = ("cuda" if torch.cuda.is_available() else "cpu")
embedding_model.to(device)
bst_r = XGBRegressor({'device': device})
bst_r.load_model('/path/to/XGBRegressor-text-classifier.json')
def score_text(text, model):
encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(device)
with torch.no_grad():
model_output = embedding_model(**encoded_input)
sentence_embedding = mean_pooling(model_output, encoded_input['attention_mask'])
embedding = F.normalize(sentence_embedding, p=2, dim=1).numpy()
score = model.predict(embedding)[0]
return score
score_text("Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente.", bst_r)
```
## Cite as ๐ค
```latex
@misc{correa2024tucanoadvancingneuraltext,
title={{Tucano: Advancing Neural Text Generation for Portuguese}},
author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
year={2024},
eprint={2411.07854},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.07854},
}
```
## Aknowlegments
We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing \& Analytics Lab.
## License
XGBRegressor-text-filter is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.
|
siddharudhh/llama_3_2_3b_onlycat1 | siddharudhh | 2024-11-13T11:13:11Z | 118 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B",
"base_model:finetune:unsloth/Llama-3.2-3B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T11:09:15Z | ---
base_model: unsloth/Llama-3.2-3B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** siddharudhh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TucanoBR/BERTimbau-large-text-filter | TucanoBR | 2024-11-13T11:12:39Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"text-quality",
"portuguese",
"pt",
"dataset:TucanoBR/GigaVerbo-Text-Filter",
"arxiv:2411.07854",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-10T17:32:14Z | ---
license: apache-2.0
datasets:
- TucanoBR/GigaVerbo-Text-Filter
language:
- pt
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- text-quality
- portuguese
widget:
- text: "Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente."
example_title: Sample 1
- text: "12 de marรงo de 2021 | Sรฃo Paulo 8 de agosto de 1999 | Porto Alegre 25 de dezembro de 2022 | Rio de Janeiro 17 de julho de 1985 | Lisboa 4 de outubro de 2010 | Belo Horizonte 23 de setembro de 1978 | Paris 14 de fevereiro de 2003 | Nova Iorque 19 de junho de 1994 | Brasรญlia 5 de novembro de 2009 | Curitiba 30 de abril de 2015 | Buenos Aires"
example_title: Sample 2
---
# BERTimbau-large-text-filter
BERTimbau-large-text-filter is a [BERT](https://huggingface.co/neuralmind/bert-large-portuguese-cased) model that can be used to score the quality of a given Portuguese text string. This model was trained on the [GigaVerbo-Text-Filter](https://huggingface.co/datasets/TucanoBR/GigaVerbo-Text-Filter) dataset.
## Details
- **Size:** 334,398,466 parameters
- **Dataset:** [GigaVerbo-Text-Filter](https://huggingface.co/datasets/TucanoBR/GigaVerbo-Text-Filter)
- **Language:** Portuguese
- **Number of Training Epochs:** 3
- **Batch size:** 128
- **Optimizer:** `torch.optim.AdamW`
- **Learning Rate:** 4e-5
This repository has the [source code](https://github.com/Nkluge-correa/Tucano) used to train this model.
## Usage
Here's an example of how to use the BERTimbau-large-text-filter:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("TucanoBR/BERTimbau-large-text-filter")
model = AutoModelForSequenceClassification.from_pretrained("TucanoBR/BERTimbau-large-text-filter")
model.to(device)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, device=device)
result = classifier("Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente.")
```
## Cite as ๐ค
```latex
@misc{correa2024tucanoadvancingneuraltext,
title={{Tucano: Advancing Neural Text Generation for Portuguese}},
author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
year={2024},
eprint={2411.07854},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.07854},
}
```
## Aknowlegments
We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing \& Analytics Lab.
## License
BERTimbau-large-text-filter is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.
|
TucanoBR/XGBClassifier-text-filter | TucanoBR | 2024-11-13T11:12:01Z | 0 | 0 | xgboost | [
"xgboost",
"text-quality",
"portuguese",
"pt",
"dataset:TucanoBR/GigaVerbo-Text-Filter",
"arxiv:2411.07854",
"license:apache-2.0",
"region:us"
] | null | 2024-06-07T14:32:34Z | ---
license: apache-2.0
datasets:
- TucanoBR/GigaVerbo-Text-Filter
language:
- pt
metrics:
- accuracy
library_name: xgboost
tags:
- text-quality
- portuguese
---
# XGBClassifier-text-filter
XGBClassifier-text-filter is a text-quality filter built on top of the [`xgboost`](https://xgboost.readthedocs.io/en/stable/) library. It uses the embeddings generated by [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) as a feature vector.
This repository has the [source code](https://github.com/Nkluge-correa/Tucano) used to train this model.
## Usage
Here's an example of how to use the XGBClassifier-text-filter:
```python
from transformers import AutoTokenizer, AutoModel
from xgboost import XGBClassifier
import torch.nn.functional as F
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/LaBSE")
embedding_model = AutoModel.from_pretrained("sentence-transformers/LaBSE")
device = ("cuda" if torch.cuda.is_available() else "cpu")
embedding_model.to(device)
bst = XGBClassifier({'device': device})
bst.load_model('/path/to/XGBClassifier-text-classifier.json')
def score_text(text, model):
encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt').to(device)
with torch.no_grad():
model_output = embedding_model(**encoded_input)
sentence_embedding = mean_pooling(model_output, encoded_input['attention_mask'])
embedding = F.normalize(sentence_embedding, p=2, dim=1).numpy()
score = model.predict(embedding)[0]
return score
score_text("Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente.", bst)
```
## Cite as ๐ค
```latex
@misc{correa2024tucanoadvancingneuraltext,
title={{Tucano: Advancing Neural Text Generation for Portuguese}},
author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
year={2024},
eprint={2411.07854},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.07854},
}
```
## Aknowlegments
We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing \& Analytics Lab.
## License
XGBClassifier-text-filter is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.
|
TucanoBR/BERTimbau-base-text-filter | TucanoBR | 2024-11-13T11:11:22Z | 228 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"text-quality",
"portuguese",
"pt",
"dataset:TucanoBR/GigaVerbo-Text-Filter",
"arxiv:2411.07854",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-11T12:00:47Z | ---
license: apache-2.0
datasets:
- TucanoBR/GigaVerbo-Text-Filter
language:
- pt
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- text-quality
- portuguese
widget:
- text: "Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente."
example_title: Sample 1
- text: "12 de marรงo de 2021 | Sรฃo Paulo 8 de agosto de 1999 | Porto Alegre 25 de dezembro de 2022 | Rio de Janeiro 17 de julho de 1985 | Lisboa 4 de outubro de 2010 | Belo Horizonte 23 de setembro de 1978 | Paris 14 de fevereiro de 2003 | Nova Iorque 19 de junho de 1994 | Brasรญlia 5 de novembro de 2009 | Curitiba 30 de abril de 2015 | Buenos Aires"
example_title: Sample 2
---
# BERTimbau-base-text-filter
BERTimbau-base-text-filter is a [BERT](https://huggingface.co/neuralmind/bert-base-portuguese-cased) model that can be used to score the quality of a given Portuguese text string. This model was trained on the [GigaVerbo-Text-Filter](https://huggingface.co/datasets/TucanoBR/GigaVerbo-Text-Filter) dataset.
## Details
- **Size:** 109,038,209 parameters
- **Dataset:** [GigaVerbo-Text-Filter](https://huggingface.co/datasets/TucanoBR/GigaVerbo-Text-Filter)
- **Language:** Portuguese
- **Number of Training Epochs:** 3
- **Batch size:** 128
- **Optimizer:** `torch.optim.AdamW`
- **Learning Rate:** 4e-5
This repository has the [source code](https://github.com/Nkluge-correa/Tucano) used to train this model.
## Usage
Here's an example of how to use the BERTimbau-base-text-filter:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TextClassificationPipeline
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("TucanoBR/BERTimbau-base-text-filter")
model = AutoModelForSequenceClassification.from_pretrained("TucanoBR/BERTimbau-base-text-filter")
model.to(device)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, device=device)
result = classifier("Os tucanos sรฃo aves que correspondem ร famรญlia Ramphastidae, vivem nas florestas tropicais da Amรฉrica Central e Amรฉrica do Sul. A famรญlia inclui cinco gรชneros e mais de quarenta espรฉcies diferentes. Possuem bicos notavelmente grandes e coloridos, que possuem a funรงรฃo de termorregulaรงรฃo para as muitas espรฉcies que passam muito tempo na copa da floresta exposta ao sol tropical quente.")
```
## Cite as ๐ค
```latex
@misc{correa2024tucanoadvancingneuraltext,
title={{Tucano: Advancing Neural Text Generation for Portuguese}},
author={Corr{\^e}a, Nicholas Kluge and Sen, Aniket and Falk, Sophia and Fatimah, Shiza},
year={2024},
eprint={2411.07854},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.07854},
}
```
## Aknowlegments
We gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing \& Analytics Lab.
## License
BERTimbau-base-text-filter is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.
|
hugosousa/classifier_smoll_135m | hugosousa | 2024-11-13T11:10:42Z | 32 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"best_valid_loss",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-30T14:49:35Z | ---
library_name: transformers
tags:
- best_valid_loss
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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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]
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## Model Card Contact
[More Information Needed] |
Rohan-G/bnb_4_bit_quantization | Rohan-G | 2024-11-13T11:05:38Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-11-13T11:00:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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vedikagoyal150903/custom-llama-code-generator | vedikagoyal150903 | 2024-11-13T11:00:33Z | 119 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T10:56:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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xxhe/esci-dpo-mistral-7b-instruct-iter-3 | xxhe | 2024-11-13T10:57:56Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T10:55:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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falan42/llama_lora_8b_medical_parallax_2_gguf | falan42 | 2024-11-13T10:46:08Z | 54 | 1 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"base_model:quantized:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-13T10:44:43Z | ---
base_model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** falan42
- **License:** apache-2.0
- **Finetuned from model :** ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/diffusionfamily_-_diffullama-gguf | RichardErkhov | 2024-11-13T10:42:35Z | 45 | 0 | null | [
"gguf",
"arxiv:2410.17891",
"endpoints_compatible",
"region:us"
] | null | 2024-11-13T06:51:04Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
diffullama - GGUF
- Model creator: https://huggingface.co/diffusionfamily/
- Original model: https://huggingface.co/diffusionfamily/diffullama/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [diffullama.Q2_K.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q2_K.gguf) | Q2_K | 2.36GB |
| [diffullama.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [diffullama.Q3_K.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q3_K.gguf) | Q3_K | 3.07GB |
| [diffullama.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [diffullama.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [diffullama.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [diffullama.Q4_0.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q4_0.gguf) | Q4_0 | 3.56GB |
| [diffullama.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [diffullama.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [diffullama.Q4_K.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q4_K.gguf) | Q4_K | 3.8GB |
| [diffullama.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [diffullama.Q4_1.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q4_1.gguf) | Q4_1 | 3.95GB |
| [diffullama.Q5_0.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q5_0.gguf) | Q5_0 | 4.33GB |
| [diffullama.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [diffullama.Q5_K.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q5_K.gguf) | Q5_K | 4.45GB |
| [diffullama.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [diffullama.Q5_1.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q5_1.gguf) | Q5_1 | 4.72GB |
| [diffullama.Q6_K.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q6_K.gguf) | Q6_K | 5.15GB |
| [diffullama.Q8_0.gguf](https://huggingface.co/RichardErkhov/diffusionfamily_-_diffullama-gguf/blob/main/diffullama.Q8_0.gguf) | Q8_0 | 6.67GB |
Original model description:
---
library_name: transformers
base_model:
- meta-llama/Llama-2-7b-hf
tags:
- llama-factory
- full
- diffusion
model-index:
- name: diffullama
results: []
license: apache-2.0
datasets:
- bigcode/starcoderdata
- cerebras/SlimPajama-627B
---
<!-- 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. -->
# diffullama
This model is a fine-tuned version of [llama2].
## Model description
Details and model loading can be seen [https://github.com/HKUNLP/DiffuLLaMA](https://github.com/HKUNLP/DiffuLLaMA).
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
```
@misc{gong2024scalingdiffusionlanguagemodels,
title={Scaling Diffusion Language Models via Adaptation from Autoregressive Models},
author={Shansan Gong and Shivam Agarwal and Yizhe Zhang and Jiacheng Ye and Lin Zheng and Mukai Li and Chenxin An and Peilin Zhao and Wei Bi and Jiawei Han and Hao Peng and Lingpeng Kong},
year={2024},
eprint={2410.17891},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.17891},
}
```
|
Beehzod/Uzbek_SpeechT5_TTS_Fine-tuning_faster | Beehzod | 2024-11-13T10:35:11Z | 77 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_17_0",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-11-13T09:39:43Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
model-index:
- name: Uzbek_SpeechT5_TTS_Fine-tuning_faster
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. -->
# Uzbek_SpeechT5_TTS_Fine-tuning_faster
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_17_0 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
amitk23/TKG3 | amitk23 | 2024-11-13T10:28:14Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T10:24:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Rarti/phi3.5_lora_merged | Rarti | 2024-11-13T10:28:13Z | 7 | 0 | null | [
"safetensors",
"phi3",
"llama-factory",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2024-11-13T10:20:32Z | ---
license: apache-2.0
tags:
- llama-factory
---
|
cuongdev/tsetmoi-1111 | cuongdev | 2024-11-13T10:22:26Z | 35 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-11-13T10:16:35Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### tsetmoi-1111 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
ksathyan/vicuna-merged-new | ksathyan | 2024-11-13T10:19:54Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-11-13T10:15:17Z | ---
library_name: transformers
tags: []
---
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ishitangupta/fastedit-model-32 | ishitangupta | 2024-11-13T10:16:25Z | 31 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-11-13T10:09:09Z | ---
library_name: diffusers
---
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VLKVLK/media-file-recognizer-tiny-llama-1.1b-v2 | VLKVLK | 2024-11-13T10:09:01Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-13T10:06:50Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
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mav23/Bielik-11B-v2-GGUF | mav23 | 2024-11-13T10:08:41Z | 67 | 0 | transformers | [
"transformers",
"gguf",
"pl",
"arxiv:2410.18565",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-13T08:48:05Z | ---
license: apache-2.0
language:
- pl
library_name: transformers
inference:
parameters:
temperature: 0.9
extra_gated_description: If you want to learn more about how you can use the model, please refer to our <a href="https://bielik.ai/terms/">Terms of Use</a>.
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-11B-v2/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2
Bielik-11B-v2 is a generative text model featuring 11 billion parameters. It is initialized from its predecessor, Mistral-7B-v0.2, and trained on 400 billion tokens.
The aforementioned model stands as a testament to the unique collaboration between the open-science/open-source project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH.
Developed and trained on Polish text corpora, which have been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment,
and more precisely, the HPC center: ACK Cyfronet AGH. The creation and training of the Bielik-11B-v2 was propelled by the support of computational grant number PLG/2024/016951, conducted on the Athena and Helios supercomputer,
enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language,
providing accurate responses and performing a variety of linguistic tasks with high precision.
โ ๏ธ This is a base model intended for further fine-tuning across most use cases. If you're looking for a model ready for chatting or following instructions out-of-the-box, please use [Bielik-11B-v.2.2-Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.2-Instruct).
๐ฅ Demo: https://chat.bielik.ai
๐ฃ๏ธ Chat Arena<span style="color:red;">*</span>: https://arena.speakleash.org.pl/
<span style="color:red;">*</span>Chat Arena is a platform for testing and comparing different AI language models, allowing users to evaluate their performance and quality.
## Model
Bielik-11B-v2 has been trained with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) using different parallelization techniques.
The model training was conducted on the Helios Supercomputer at the ACK Cyfronet AGH, utilizing 256 NVidia GH200 cards.
The training dataset was composed of Polish texts collected and made available through the [SpeakLeash](https://speakleash.org/) project, as well as a subset of CommonCrawl data. We used 200 billion tokens (over 700 GB of plain text) for two epochs of training.
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/) & [ACK Cyfronet AGH](https://www.cyfronet.pl/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Initialized from:** [Mistral-7B-v0.2](https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar)
* **License:** Apache 2.0 and [Terms of Use](https://bielik.ai/terms/)
* **Model ref:** speakleash:45b6efdb701991181a05968fc53d2a8e
### Quality evaluation
An XGBoost classification model was prepared and created to evaluate the quality of texts in native Polish language. It is based on 93 features, such as the ratio of out-of-vocabulary words to all words (OOVs), the number of nouns, verbs, average sentence length etc. The model outputs the category of a given document (either HIGH, MEDIUM or LOW) along with the probability. This approach allows implementation of a dedicated pipeline to choose documents, from which we've used entries with HIGH quality index and probability exceeding 90%.
This filtration and appropriate selection of texts enable the provision of a condensed and high-quality database of texts in Polish for training purposes.
### Quickstart
This model can be easily loaded using the AutoModelForCausalLM functionality.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "speakleash/Bielik-11B-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
In order to reduce the memory usage, you can use smaller precision (`bfloat16`).
```python
import torch
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
```
And then you can use HuggingFace Pipelines to generate text:
```python
import transformers
text = "Najwaลผniejszym celem czลowieka na ziemi jest"
pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
sequences = pipeline(max_new_tokens=100, do_sample=True, top_k=50, eos_token_id=tokenizer.eos_token_id, text_inputs=text)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
Generated output:
> Najwaลผniejszym celem czลowieka na ziemi jest ลผycie w pokoju, harmonii i miลoลci. Dla kaลผdego z nas bardzo waลผne jest, aby otaczaฤ siฤ kochanymi osobami.
## Evaluation
Models have been evaluated on two leaderboards: [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) and [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The Open PL LLM Leaderboard uses a 5-shot evaluation and focuses on NLP tasks in Polish, while the Open LLM Leaderboard evaluates models on various English language tasks.
### Open PL LLM Leaderboard
The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Average column is an average score among all tasks normalized by baseline scores.
| Model | Parameters (B) | Average |
|------------------------|------------|---------|
| Meta-Llama-3-70B | 70 | 62.07 |
| Qwen1.5-72B | 72 | 61.11 |
| Meta-Llama-3.1-70B | 70 | 60.87 |
| Mixtral-8x22B-v0.1 | 141 | 60.75 |
| Qwen1.5-32B | 32 | 58.71 |
| **Bielik-11B-v2** | **11** | **58.14** |
| Qwen2-7B | 7 | 49.39 |
| SOLAR-10.7B-v1.0 | 10.7 | 47.54 |
| Mistral-Nemo-Base-2407 | 12 | 47.28 |
| internlm2-20b | 20 | 47.15 |
| Meta-Llama-3.1-8B | 8 | 43.77 |
| Meta-Llama-3-8B | 8 | 43.30 |
| Mistral-7B-v0.2 | 7 | 38.81 |
| Bielik-7B-v0.1 | 7 | 34.34 |
| Qra-13b | 13 | 33.90 |
| Qra-7b | 7 | 16.60 |
The results from the Open PL LLM Leaderboard show that the Bielik-11B-v2 model, with 11 billion parameters, achieved an average score of 58.14. This makes it the best performing model among those under 20B parameters, outperforming the second-best model in this category by an impressive 8.75 percentage points. This significant lead not only places it ahead of its predecessor, the Bielik-7B-v0.1 (which scored 34.34), but also demonstrates its superiority over other larger models. The substantial improvement highlights the remarkable advancements and optimizations made in this newer version.
Other Polish models listed include Qra-13b and Qra-7b, scoring 33.90 and 16.60 respectively, indicating that Bielik-11B-v2 outperforms these models by a considerable margin.
Additionally, the Bielik-11B-v2 was initialized from the weights of Mistral-7B-v0.2, which itself scored 38.81, further demonstrating the effective enhancements incorporated into the Bielik-11B-v2 model.
### Open LLM Leaderboard
The Open LLM Leaderboard evaluates models on various English language tasks, providing insights into the model's performance across different linguistic challenges.
| Model | AVG | arc_challenge | hellaswag | truthfulqa_mc2 | mmlu | winogrande | gsm8k |
|-------------------------|-------|---------------|-----------|----------------|-------|------------|-------|
| **Bielik-11B-v2** | **65.87** | 60.58 | 79.84 | 46.13 | 63.06 | 77.82 | 67.78 |
| Mistral-7B-v0.2 | 60.37 | 60.84 | 83.08 | 41.76 | 63.62 | 78.22 | 34.72 |
| Bielik-7B-v0.1 | 49.98 | 45.22 | 67.92 | 47.16 | 43.20 | 66.85 | 29.49 |
The results from the Open LLM Leaderboard demonstrate the impressive performance of Bielik-11B-v2 across various NLP tasks. With an average score of 65.87, it significantly outperforms its predecessor, Bielik-7B-v0.1, and even surpasses Mistral-7B-v0.2, which served as its initial weight basis.
Key observations:
1. Bielik-11B-v2 shows substantial improvements in most categories compared to Bielik-7B-v0.1, highlighting the effectiveness of the model's enhancements.
2. It performs exceptionally well in tasks like hellaswag (common sense reasoning), winogrande (commonsense reasoning), and gsm8k (mathematical problem-solving), indicating its versatility across different types of language understanding and generation tasks.
3. While Mistral-7B-v0.2 outperforms in truthfulqa_mc2, Bielik-11B-v2 maintains competitive performance in this truth-discernment task.
Although Bielik-11B-v2 was primarily trained on Polish data, it has retained and even improved its ability to understand and operate in English, as evidenced by its strong performance across these English-language benchmarks. This suggests that the model has effectively leveraged cross-lingual transfer learning, maintaining its Polish language expertise while enhancing its English language capabilities.
## Limitations and Biases
Bielik-11B-v2 is not intended for deployment without fine-tuning. It should not be used for human-facing interactions without further guardrails and user consent.
Bielik-11B-v2 can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-11B-v2 was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs.
## Citation
Please cite this model using the following format:
```
@misc{Bielik11Bv2b,
title = {Bielik-11B-v2 model card},
author = {Ociepa, Krzysztof and Flis, ลukasz and Wrรณbel, Krzysztof and Gwoลบdziej, Adrian and {SpeakLeash Team} and {Cyfronet Team}},
year = {2024},
url = {https://huggingface.co/speakleash/Bielik-11B-v2},
note = {Accessed: 2024-08-28},
urldate = {2024-08-28}
}
@unpublished{Bielik11Bv2a,
author = {Ociepa, Krzysztof and Flis, ลukasz and Kinas, Remigiusz and Gwoลบdziej, Adrian and Wrรณbel, Krzysztof},
title = {Bielik: A Family of Large Language Models for the Polish Language - Development, Insights, and Evaluation},
year = {2024},
}
@misc{ociepa2024bielik7bv01polish,
title={Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation},
author={Krzysztof Ociepa and ลukasz Flis and Krzysztof Wrรณbel and Adrian Gwoลบdziej and Remigiusz Kinas},
year={2024},
eprint={2410.18565},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.18565},
}
```
## Responsible for training the model
* [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training
* [ลukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training
* [Adrian Gwoลบdziej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data cleaning and quality
* [Krzysztof Wrรณbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks
The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model:
[Grzegorz Urbanowicz](https://www.linkedin.com/in/grzegorz-urbanowicz-05823469/),
[Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/),
[Jacek Chwiลa](https://www.linkedin.com/in/jacek-chwila/),
[Szymon Baczyลski](https://www.linkedin.com/in/szymon-baczynski/),
[Paweล Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/),
[Aleksander Smywiลski-Pohl](https://www.linkedin.com/in/apohllo/).
Members of the ACK Cyfronet AGH team providing valuable support and expertise:
[Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/),
[Marek Magryล](https://www.linkedin.com/in/magrys/).
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/pv4brQMDTy).
|
Helios9/NCBI_NER | Helios9 | 2024-11-13T10:08:08Z | 21 | 1 | null | [
"safetensors",
"deberta-v2",
"NER",
"phenotypes",
"diseases",
"bio",
"classification",
"token-classification",
"en",
"dataset:ncbi/pubmed",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:unknown",
"region:us"
] | token-classification | 2024-11-13T09:51:00Z | ---
license: unknown
datasets:
- ncbi/pubmed
language:
- en
metrics:
- f1
base_model:
- microsoft/deberta-v3-base
pipeline_tag: token-classification
tags:
- NER
- phenotypes
- diseases
- bio
- classification
---
**How to Use the Model for Inference:**
You can use the Hugging Face `pipeline` for easy inference:
```python
from transformers import pipeline
# Load the model
model_path = "venkatd/NCBI_NER"
pipe = pipeline(
task="token-classification",
model=model_path,
tokenizer=model_path,
aggregation_strategy="simple"
)
# Test the pipeline
text = ("A 48-year-old female presented with vaginal bleeding and abnormal Pap smears. "
"Upon diagnosis of invasive non-keratinizing SCC of the cervix, she underwent a radical "
"hysterectomy with salpingo-oophorectomy which demonstrated positive spread to the pelvic "
"lymph nodes and the parametrium.")
result = pipe(text)
print(result)
```
**Output Example:**
The output will be entity type of Disease, score, and start/end positions in the text. Hereโs a sample output format:
```json
[
{
"entity_group": "Disease",
"score": 0.98,
"word": "SCC of the cervix",
"start": 121,
"end": 139
},
...
]
```
**Model Summary and Training Details**
### Model Architecture
- **Base Model**: `microsoft/deberta-v3-base`
- **Task**: Token Classification for Named Entity Recognition (NER) with a focus on disease entities.
- **Number of Labels**: 3 (O, B-Disease, I-Disease)
### Dataset
- **Dataset**: NCBI Disease Corpus
- **Description**: The NCBI Disease corpus is a specialized medical dataset that includes 793 PubMed abstracts. It is structured to help in identifying disease mentions within scientific literature, and each mention is annotated with disease concepts from the MeSH (Medical Subject Headings) or OMIM (Online Mendelian Inheritance in Man) databases.
- **Split**:
- Training Set: 593 abstracts
- Development (Validation) Set: 100 abstracts
- Test Set: 100 abstracts
### Training Details
- **Training Steps**: The model was trained using a cross-entropy loss function for token classification tasks. To optimize performance, we used gradient accumulation to achieve a stable loss and improve resource efficiency.
- **Gradient Accumulation**: 2 steps
- **Batch Size**: 8
- **Device**: Trained on a GPU if available, using mixed-precision training for better performance.
### Optimizer and Learning Rate Scheduler
- **Optimizer**: AdamW
- **Learning Rate**: 1e-5
- **Betas**: (0.9, 0.999)
- **Epsilon**: 1e-8
- **Learning Rate Scheduler**: Cosine Scheduler with Warmup
- **Warmup Steps**: 10% of total training steps
- **Total Training Steps**: Calculated as `len(train_loader) * num_epochs`
### Epochs and Validation
- **Epochs**: 5
- **Training and Validation Loss**: The model achieved a stable loss over 5 epochs, with the best validation loss recorded. The best model based on validation loss was saved for evaluation.
### Evaluation and Performance
- **Test Dataset F1 Score**: 0.9772
- **Evaluation Metric**: F1 score, which indicates the balance between precision and recall, was used as the primary metric to assess the modelโs performance.
|
Rohan-G/partial_quantization_from_scratch | Rohan-G | 2024-11-13T10:07:46Z | 153 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] | text-generation | 2024-11-13T09:58:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
NiloofarMomeni/distilhubert-finetuned-VD | NiloofarMomeni | 2024-11-13T10:04:54Z | 162 | 0 | transformers | [
"transformers",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-06-03T14:32:16Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-VD
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8933256172839507
---
<!-- 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. -->
# distilhubert-finetuned-VD
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7226
- Accuracy: 0.8933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3302 | 1.0 | 195 | 0.3716 | 0.8800 |
| 0.6059 | 2.0 | 390 | 0.5195 | 0.8090 |
| 0.4938 | 3.0 | 585 | 1.0102 | 0.6260 |
| 0.836 | 4.0 | 780 | 1.1662 | 0.6742 |
| 0.2234 | 5.0 | 975 | 0.6792 | 0.8389 |
| 0.1444 | 6.0 | 1170 | 0.9137 | 0.8239 |
| 0.2986 | 7.0 | 1365 | 0.7987 | 0.8623 |
| 0.0004 | 8.0 | 1560 | 1.5075 | 0.7687 |
| 0.0005 | 9.0 | 1755 | 0.7226 | 0.8933 |
| 0.0002 | 10.0 | 1950 | 0.8246 | 0.8829 |
| 0.0002 | 11.0 | 2145 | 1.4227 | 0.8129 |
| 0.0001 | 12.0 | 2340 | 1.0478 | 0.8665 |
| 0.0001 | 13.0 | 2535 | 1.3328 | 0.8322 |
| 0.0001 | 14.0 | 2730 | 1.3480 | 0.8347 |
| 0.0001 | 15.0 | 2925 | 1.3559 | 0.8370 |
| 0.0 | 16.0 | 3120 | 1.3589 | 0.8407 |
| 0.0 | 17.0 | 3315 | 1.3706 | 0.8410 |
| 0.0 | 18.0 | 3510 | 1.3831 | 0.8410 |
| 0.0 | 19.0 | 3705 | 1.3954 | 0.8410 |
| 0.0 | 20.0 | 3900 | 1.4027 | 0.8412 |
| 0.0 | 21.0 | 4095 | 1.4132 | 0.8409 |
| 0.0 | 22.0 | 4290 | 1.4218 | 0.8407 |
| 0.0 | 23.0 | 4485 | 1.4272 | 0.8407 |
| 0.0 | 24.0 | 4680 | 1.4321 | 0.8399 |
| 0.0 | 25.0 | 4875 | 1.4337 | 0.8399 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Shah1st/mountain-ner-model | Shah1st | 2024-11-13T10:03:46Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-16T22:14:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text.
The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.
Features:
Fine-tuned on a custom dataset that includes sentences both with and without mountain names.
Uses focal loss to handle class imbalance, which ensures the model focuses on correctly classifying rare mountain names.
Token-level classification for identifying the B-MOUNTAIN, I-MOUNTAIN, and O (non-entity) labels.
Balances training between sentences with mountains (80%) and without mountains (20%).
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub.
- **Developed by:** Oleksandr Kharytonov
- **Model type:** BERT
- **Language(s) (NLP):** Python
- **License:** MIT
- **Finetuned from model [optional]:** https://huggingface.co/dslim/bert-large-NER
-
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Shah1st/mountain-ner
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained('./saved_model')
model = AutoModelForTokenClassification.from_pretrained('./saved_model')
```
### 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.
## How to Get Started with the Model
Use the github below to get started with the model.
https://github.com/Shah1st/mountain-ner
## 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. -->
"DFKI-SLT/few-nerd", "supervised"
Filter for sentences with 'fine_ner_tags' == 24 (mountains)
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
'eval_loss': 0.009154710918664932, 'eval_macro_f1': 0.8952192988290304, 'eval_accuracy': 0.9746226793108054
### Testing Data, Factors & Metrics
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
macro F1: 0.895
Accuracy: 0.974
#### Summary
This project involves fine-tuning a BERT-based model (dslim/bert-large-NER) to perform Named Entity Recognition (NER) on mountain names in text. The model has been trained to identify mentions of mountain names and differentiate them from other geographic entities or non-entities.
|
Subsets and Splits