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jackkma/Qwen-Qwen1.5-0.5B-1725430936 | jackkma | "2024-09-04T06:22:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:22:16Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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.
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xueyj/Qwen-Qwen1.5-0.5B-1725430966 | xueyj | "2024-09-04T06:23:27Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:22:47Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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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).
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downtown1/Qwen-Qwen1.5-1.8B-1725430973 | downtown1 | "2024-09-04T06:22:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:22:53Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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.
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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).
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jackkma/Qwen-Qwen1.5-1.8B-1725431003 | jackkma | "2024-09-04T06:23:29Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:23:23Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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).
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- PEFT 0.12.0 |
kromer9margarete337/Qwen-Qwen1.5-0.5B-1725431017 | kromer9margarete337 | "2024-09-04T06:23:40Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:23:39Z" | Entry not found |
HanyuYuzuru/xlm-roberta-base-finetuned-panx-de | HanyuYuzuru | "2024-09-04T06:23:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:23:41Z" | Entry not found |
AKDATA1/Qwen-Qwen1.5-7B-1725431069 | AKDATA1 | "2024-09-04T06:24:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-09-04T06:24:29Z" | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
# Model Card for Model ID
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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.
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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).
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### Framework versions
- PEFT 0.12.0 |
xueyj/Qwen-Qwen1.5-0.5B-1725431071 | xueyj | "2024-09-04T06:24:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:24:31Z" | Entry not found |
jackkma/google-gemma-2b-1725431086 | jackkma | "2024-09-04T06:24:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:24:46Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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Krabat/Qwen-Qwen1.5-1.8B-1725431128 | Krabat | "2024-09-04T06:25:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:25:28Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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asdfre453/eahl | asdfre453 | "2024-09-04T07:16:21Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:25:40Z" | ---
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
--- |
xueyj/Qwen-Qwen1.5-1.8B-1725431142 | xueyj | "2024-09-04T06:25:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:25:42Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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downtown1/Qwen-Qwen1.5-0.5B-1725431159 | downtown1 | "2024-09-04T06:26:05Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:25:59Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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AKDATA1/google-gemma-7b-1725431174 | AKDATA1 | "2024-09-04T06:26:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:26:14Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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kvya143/Prakash.Raj | kvya143 | "2024-09-04T06:26:37Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-09-04T06:26:37Z" | ---
license: apache-2.0
---
|
mcyyxsky/xiajing | mcyyxsky | "2024-09-04T06:54:48Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:27:04Z" | ---
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
--- |
jackkma/google-gemma-7b-1725431234 | jackkma | "2024-09-04T06:27:32Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:27:14Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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Songnek/test_llama2 | Songnek | "2024-09-04T06:35:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | "2024-09-04T06:27:32Z" | ---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
---
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Krabat/google-gemma-2b-1725431291 | Krabat | "2024-09-04T06:28:14Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:28:11Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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senjin1/Qwen-Qwen1.5-0.5B-1725431316 | senjin1 | "2024-09-04T06:28:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:28:36Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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senjin1/Qwen-Qwen1.5-1.8B-1725431382 | senjin1 | "2024-09-04T06:29:46Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:29:42Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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linger2334/Qwen-Qwen1.5-7B-1725431402 | linger2334 | "2024-09-04T06:30:12Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-09-04T06:30:05Z" | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
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Actlin/Whispher_small_dys_mild_org_prompts | Actlin | "2024-09-04T06:47:47Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-09-04T06:30:42Z" | Entry not found |
senjin1/google-gemma-2b-1725431458 | senjin1 | "2024-09-04T06:31:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:30:58Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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Krabat/google-gemma-7b-1725431493 | Krabat | "2024-09-04T06:31:37Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:31:33Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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kromer9margarete337/Qwen-Qwen1.5-1.8B-1725431507 | kromer9margarete337 | "2024-09-04T06:31:54Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:31:49Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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linger2334/google-gemma-2b-1725431535 | linger2334 | "2024-09-04T06:32:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:32:18Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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senjin1/google-gemma-7b-1725431610 | senjin1 | "2024-09-04T06:33:44Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:33:31Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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aarondeveloper/AtomicGPT | aarondeveloper | "2024-09-04T06:33:35Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:33:35Z" | Entry not found |
tona3738/my-lang-adapter | tona3738 | "2024-09-19T08:01:09Z" | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"bert",
"region:us"
] | null | "2024-09-04T06:34:25Z" | ---
tags:
- adapter-transformers
- bert
---
# Adapter `tona3738/my-lang-adapter` for robzchhangte/MizBERT
An [adapter](https://adapterhub.ml) for the `robzchhangte/MizBERT` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("robzchhangte/MizBERT")
adapter_name = model.load_adapter("tona3738/my-lang-adapter", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
888facaiboy/Qwen-Qwen1.5-0.5B-1725431704 | 888facaiboy | "2024-09-04T06:35:08Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:35:04Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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downtown1/Qwen-Qwen1.5-1.8B-1725431735 | downtown1 | "2024-09-04T06:35:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:35:35Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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HFit1/model-9-04 | HFit1 | "2024-09-04T06:46:55Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2024-09-04T06:36:07Z" | Entry not found |
888facaiboy/Qwen-Qwen1.5-1.8B-1725431769 | 888facaiboy | "2024-09-04T06:36:13Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:36:09Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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linger2334/google-gemma-7b-1725431793 | linger2334 | "2024-09-04T06:36:43Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:36:36Z" | ---
base_model: google/gemma-7b
library_name: peft
---
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888facaiboy/google-gemma-2b-1725431849 | 888facaiboy | "2024-09-04T06:37:39Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:37:29Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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xueyj/Qwen-Qwen1.5-1.8B-1725431912 | xueyj | "2024-09-04T06:38:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:38:32Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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xueyj/Qwen-Qwen1.5-0.5B-1725431924 | xueyj | "2024-09-04T06:41:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:38:44Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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downtown1/Qwen-Qwen1.5-0.5B-1725431950 | downtown1 | "2024-09-04T06:39:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:39:10Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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linger2334/Qwen-Qwen1.5-0.5B-1725431950 | linger2334 | "2024-09-04T06:39:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:39:13Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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- PEFT 0.12.0 |
geonmin-kim/gemma-2-2b-it-q0f16-MLC | geonmin-kim | "2024-09-04T06:41:48Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:39:37Z" | Entry not found |
888facaiboy/google-gemma-7b-1725431998 | 888facaiboy | "2024-09-04T06:40:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:39:58Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
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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).
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### Framework versions
- PEFT 0.12.0 |
KiraOfuru/Nyantcha-81 | KiraOfuru | "2024-09-04T07:26:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:40:45Z" | Entry not found |
guojianbinhaha/sdxl-naruto-model | guojianbinhaha | "2024-09-04T06:40:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:40:53Z" | Entry not found |
meenaldharaatech/test1_model | meenaldharaatech | "2024-09-04T07:01:19Z" | 0 | 0 | null | [
"safetensors",
"distilbert",
"region:us"
] | null | "2024-09-04T06:40:57Z" | Entry not found |
Utkarsha666/Llama-2-7b-chat-nepali-health-chat | Utkarsha666 | "2024-09-04T06:52:09Z" | 0 | 0 | null | [
"safetensors",
"llama",
"license:mit",
"region:us"
] | null | "2024-09-04T06:41:07Z" | ---
license: mit
---
NOTE:- The model is made for educational purpose only. Only 1 epoch trained due to Free GPU Limitation, the results are not great.
DATASET USED:- Chhabi/testing-dataset-llama2-nepali-health
|
xueyj/google-gemma-2b-1725432120 | xueyj | "2024-09-04T06:42:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:42:00Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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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.
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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 -->
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).
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- PEFT 0.12.0 |
linger2334/Qwen-Qwen1.5-1.8B-1725432119 | linger2334 | "2024-09-04T06:42:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:42:02Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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downtown1/Qwen-Qwen1.5-1.8B-1725432132 | downtown1 | "2024-09-04T06:42:16Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:42:12Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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xueyj/google-gemma-2b-1725432176 | xueyj | "2024-09-04T06:45:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:42:56Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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knojavier/knojavier1 | knojavier | "2024-09-04T07:32:37Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:43:35Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
lionking927/sn11-v03-0343 | lionking927 | "2024-09-04T06:44:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:44:00Z" | Entry not found |
jerseyjerry/Qwen-Qwen2-1.5B-1725432246 | jerseyjerry | "2024-09-04T06:44:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2-1.5B",
"base_model:adapter:Qwen/Qwen2-1.5B",
"region:us"
] | null | "2024-09-04T06:44:06Z" | ---
base_model: Qwen/Qwen2-1.5B
library_name: peft
---
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Krabat/Qwen-Qwen1.5-0.5B-1725432263 | Krabat | "2024-09-04T06:44:25Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:44:23Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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xueyj/Qwen-Qwen1.5-1.8B-1725432310 | xueyj | "2024-09-04T06:45:26Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:45:10Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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downtown1/Qwen-Qwen1.5-0.5B-1725432324 | downtown1 | "2024-09-04T06:45:29Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:45:24Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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xueyj/Qwen-Qwen1.5-0.5B-1725432333 | xueyj | "2024-09-04T06:45:42Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:45:33Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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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).
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- PEFT 0.12.0 |
manuelgoicolea/mgoico | manuelgoicolea | "2024-09-04T07:59:35Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:45:57Z" | ---
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
--- |
forozande/hasan10 | forozande | "2024-09-04T06:53:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:47:47Z" | Entry not found |
danyelcuervo/danyel-lora | danyelcuervo | "2024-09-04T07:15:44Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:47:48Z" | ---
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
--- |
xueyj/Qwen-Qwen1.5-1.8B-1725432498 | xueyj | "2024-09-04T06:48:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:48:19Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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).
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downtown1/Qwen-Qwen1.5-1.8B-1725432522 | downtown1 | "2024-09-04T06:48:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:48:42Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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).
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mutiullahj/CSQA | mutiullahj | "2024-09-04T06:48:50Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-09-04T06:48:50Z" | ---
license: mit
---
|
khanhnn55/nas27a42ab40e35 | khanhnn55 | "2024-09-04T16:15:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:48:53Z" | Entry not found |
xueyj/google-gemma-7b-1725432559 | xueyj | "2024-09-04T06:49:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:49:19Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.12.0 |
alireza10483/ali | alireza10483 | "2024-09-04T06:49:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:49:23Z" | Entry not found |
phoenix-1708/MAJIC_V2 | phoenix-1708 | "2024-09-27T02:47:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:49:35Z" | Entry not found |
daswer123/flux_test_loras | daswer123 | "2024-09-22T23:11:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:49:44Z" | Entry not found |
xueyj/google-gemma-2b-1725432600 | xueyj | "2024-09-04T06:55:33Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:50:00Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.12.0 |
ChandrilBasu/FalaiLora | ChandrilBasu | "2024-09-04T06:50:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:50:34Z" | Entry not found |
apple9855/dqn-SpaceInvadersNoFrameskip-v4 | apple9855 | "2024-09-04T06:51:01Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-09-04T06:50:37Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 317.00 +/- 186.15
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga apple9855 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga apple9855 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga apple9855
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 2),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 8),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
downtown1/Qwen-Qwen1.5-0.5B-1725432711 | downtown1 | "2024-09-04T06:51:57Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:51:52Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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## Model Details
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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.
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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).
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qboodp/nllb-200-600M-onmt-ct2 | qboodp | "2024-09-04T06:53:07Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-09-04T06:53:07Z" | ---
license: mit
---
|
yunhuijang/q6tpkkr6 | yunhuijang | "2024-09-13T15:10:29Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-09-04T06:53:45Z" | Entry not found |
S1mpleAF/whisper-large-v3-hi | S1mpleAF | "2024-09-04T06:54:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:54:04Z" | Entry not found |
xueyj/Qwen-Qwen1.5-0.5B-1725432886 | xueyj | "2024-09-04T06:54:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:54:46Z" | Entry not found |
xueyj/google-gemma-7b-1725432887 | xueyj | "2024-09-04T06:55:37Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T06:54:47Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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).
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- PEFT 0.12.0 |
downtown1/Qwen-Qwen1.5-1.8B-1725432888 | downtown1 | "2024-09-04T06:54:53Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:54:48Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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.
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[More Information Needed]
## Training Details
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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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).
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Krabat/Qwen-Qwen1.5-1.8B-1725433034 | Krabat | "2024-09-04T06:57:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:57:14Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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fhofmann/VertebralBodiesCT-ResEncM | fhofmann | "2024-09-04T14:12:33Z" | 0 | 0 | null | [
"medical",
"image-segmentation",
"dataset:fhofmann/VertebralBodiesCT-Labels",
"arxiv:2404.09556",
"license:cc-by-sa-4.0",
"region:us"
] | image-segmentation | "2024-09-04T06:57:32Z" | ---
license: cc-by-sa-4.0
datasets:
- fhofmann/VertebralBodiesCT-Labels
pipeline_tag: image-segmentation
tags:
- medical
---
# Model Card for VertebralBodiesCT-ResEncM
VertebralBodiesCT-ResEncM model is a deep learning segmentation model built using the [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) framework.
It is designed to identify thoracic and lumbar vertebral bodies in CT scans, specifically excluding the vertebral arches and spinous processes.
**This is the smaller, faster model trained using a single fold with the [ResEnc M presets](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md#how-to-use-the-new-presets).
A larger, more performant version using 5 folds with the ResEnc L presets is available [here](https://huggingface.co/fhofmann/VertebralBodiesCT-ResEncL).**
## Model Details
This model was developed for segmenting thoracic and lumbar vertebral bodies in CT scans, with a focus on anatomical landmark identification for applications such as body composition analysis and sarcopenia assessment.
It was trained on a dataset derived from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator/) and [VerSe](https://github.com/anjany/verse) datasets, which were refined [to focus solely on the vertebral bodies](https://huggingface.co/datasets/fhofmann/VertebralBodiesCT-Labels/blob/main/suppl/3_vertebralbodies.md)
The model is available under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/) and accompanied by the [training labels](https://huggingface.co/datasets/fhofmann/VertebralBodiesCT-Labels) and a [pipeline](https://github.com/fohofmann/BodyComposition) for body composition analysis.
## Uses
The VertebralBodiesCT-ResEncM model is intended for segmenting thoracic and lumbar vertebral bodies in CT scans.
For instance after integration into a [pipeline](https://github.com/fohofmann/BodyComposition), it can be used for localizing measurements relative to anatomical landmarks.
The model excludes vertebral arches and spinous processes, making it unsuitable for whole spine analysis.
It is not intended for clinical application.
## Bias, Risks, and Limitations
- Data Source Bias: The model, despite being trained in a heterogenous dataset, may not fully represent diverse populations, imaging techniques, or scanner types.
- Misfunction in Pathologies: The model may perform poorly in cases with uncommon or complex spinal pathologies not well-represented in the dataset.
- Persistent Labeling Errors: Some labeling errors may persist in the training dataset. This could cause segmentation errors, especially in cases with transitional vertebrae.
- Exclusion of Cervical Spine: The model is designed only for thoracic and lumbar vertebrae and the sacrum.
- Exclusion of Vertebral Arches and Spinous Processes: The labels focus solely on vertebral bodies, excluding vertebral arches and spinous processes.
- No Clinical Usage: This model is intended solely for research purposes.
## How to Get Started with the Model
The model is a standard, unmodified [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) model.
Installation instructions for nnU-Net can be found [here](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/installation_instructions.md), with more information on inference [here](https://github.com/MIC-DKFZ/nnUNet/tree/master/nnunetv2/inference).
We provide a integration into a [Body Composition analysis pipeline](https://github.com/fohofmann/BodyComposition).
## Training Details
The training dataset consists of 1216 labels corresponding to CT scans derived from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator/) and [VerSe](https://github.com/anjany/verse) datasets.
Detailed information on the labeling process can be found [here](https://huggingface.co/datasets/fhofmann/VertebralBodiesCT-Labels).
The model segments thoracic vertebrae T1 to T12 (labels 1-12), lumbar vertebrae L1 to L5 (labels 13-17), and the sacrum (label 19), with optional vertebrae such as T13 (label 21) or L6 (label 18).
Training was conducted using a single fold over 1000 epochs.
The [dataset_fingerprint.json](nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres/dataset_fingerprint.json), [model plan with model configuration and hyperparameters](nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres/plans.json) and [training logs](https://huggingface.co/fhofmann/VertebralBodiesCT-ResEncM/tree/main/nnUNetTrainer__nnUNetResEncUNetMPlans__3d_fullres/fold_all/logs) are available with the model.
## Evaluation
The model's performance metrics were assessed in a separate test dataset, and its clinical value in identifying the third lumbar vertebra (L3) was explored in an [independent oncological dataset](https://doi.org/10.25737/SZ96-ZG60). Model metrics are available [here](metrics.md), and detailed results are described in [our paper](#Citation).
## Technical Requirements
According to the [residual encoder UNet presets](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md), the nnU-Net ResEnc M requires a GPU with 9-11GB VRAM.
## Citation
We deeply appreciate the foundational work of the TotalSegmentator and VerSe authors.
The generation of the labels would not have been possible without their open-source contributions.
If you use this model, please cite the following publications:
```
Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M. (2023). TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024
```
```
Sekuboyina, A., Husseini, M.E., Bayat, A., Löffler, M., Liebl, H., Li, H., Tetteh, G., Kukačka, J., Payer, C., Štern, D., Urschler, M., Chen, M., Cheng, D., Lessmann, N., Hu, Y., Wang, T., Yang, D., Xu, D., Ambellan, F., Amiranashvili, T., Ehlke, M., Lamecker, H., Lehnert, S., Lirio, M., Pérez de Olaguer, N., Ramm, H., Sahu, M., Tack, A., Zachow, S., Jiang, T., Ma, X., Angerman, C., Wang, X., Brown, K., Kirszenberg, A., Puybareau, É., Chen, D., Bai, Y., Rapazzo, B.H., Yeah, T., Zhang, A., Xu, S., Hou, F., He, Z., Zeng, C., Xiangshang, Z., Liming, X., Netherton, T.J., Mumme, R.P., Court, L.E., Huang, Z., He, C., Wang, L.-W., Ling, S.H., Huỳnh, L.D., Boutry, N., Jakubicek, R., Chmelik, J., Mulay, S., Sivaprakasam, M., Paetzold, J.C., Shit, S., Ezhov, I., Wiestler, B., Glocker, B., Valentinitsch, A., Rempfler, M., Menze, B.H., Kirschke, J.S. (2021). VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images. Medical Image Analysis. https://doi.org/10.1016/j.media.2021.102166
```
```
Haubold, J., Baldini, G., Parmar, V., Schaarschmidt, B.M., Koitka, S., Kroll, L., van Landeghem, N., Umutlu, L., Forsting, M., Nensa, F., Hosch, R. (2023). BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care. Investigative Radiology. https://doi.org/10.1097/RLI.0000000000001040
```
Since the model is based on the [nnU-Net framework](https://github.com/MIC-DKFZ/nnUNet) and the [residual encoder UNet presets](https://github.com/MIC-DKFZ/nnUNet/blob/master/documentation/resenc_presets.md), please cite the following papers:
```
Isensee, F., Jaeger, P.F., Kohl, S. A., Petersen, J., Maier-Hein, K.H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211. https://doi.org/10.1038/s41592-020-01008-z
```
```
Isensee, F., Wald, T., Ulrich, C., Baumgartner, M. , Roy, S., Maier-Hein, K., Jaeger, P. (2024). nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation. arXiv preprint arXiv:2404.09556.
```
Please cite our dataset:
```
Hofmann F.O. et al. Thoracic & lumbar vertebral body labels corresponding to 1460 public CT scans. https://huggingface.co/datasets/fhofmann/VertebralBodiesCT-Labels/
```
## Contact
Any feedback, questions or recommendations? Feel free to [contact us](https://huggingface.co/fhofmann/VertebralBodiesCT-ResEncM/discussions)! 🤗 |
DREAMER2049/baigu_flux_lora | DREAMER2049 | "2024-09-04T10:55:20Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-09-04T06:57:35Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
biboombi/Qwen-Qwen1.5-0.5B-1725433088 | biboombi | "2024-09-04T06:58:12Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:58:08Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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xueyj/Qwen-Qwen1.5-0.5B-1725433091 | xueyj | "2024-09-04T06:58:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:58:11Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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downtown1/Qwen-Qwen1.5-0.5B-1725433091 | downtown1 | "2024-09-04T06:58:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T06:58:12Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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xueyj/Qwen-Qwen1.5-1.8B-1725433133 | xueyj | "2024-09-04T06:59:03Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:58:53Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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biboombi/Qwen-Qwen1.5-1.8B-1725433147 | biboombi | "2024-09-04T06:59:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T06:59:07Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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).
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- PEFT 0.12.0 |
yangtao9009/PASD-SDXL | yangtao9009 | "2024-09-04T10:52:16Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-09-04T06:59:48Z" | ---
license: apache-2.0
---
|
shan168/qishiyouyu | shan168 | "2024-09-04T06:59:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T06:59:56Z" | Entry not found |
Krabat/google-gemma-2b-1725433197 | Krabat | "2024-09-04T07:00:00Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T06:59:57Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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.
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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).
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- PEFT 0.12.0 |
ohyuna09/sjimissssssh | ohyuna09 | "2024-09-04T07:00:52Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-09-04T07:00:04Z" | ---
license: openrail
---
|
joony0830/llama07-test | joony0830 | "2024-09-04T07:00:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T07:00:09Z" | Entry not found |
downtown1/Qwen-Qwen1.5-1.8B-1725433274 | downtown1 | "2024-09-04T07:01:19Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T07:01:14Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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).
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xueyj/Qwen-Qwen1.5-1.8B-1725433276 | xueyj | "2024-09-04T07:01:28Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-09-04T07:01:17Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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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.
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.12.0 |
mlashcorp/artur_LORA | mlashcorp | "2024-09-04T07:01:37Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2024-09-04T07:01:18Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: a photo of ACPCR person
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. -->
# SDXL LoRA DreamBooth - mlashcorp/artur_LORA
<Gallery />
## Model description
These are mlashcorp/artur_LORA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of ACPCR person to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](mlashcorp/artur_LORA/tree/main) them in the Files & versions tab.
## 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] |
xueyj/google-gemma-2b-1725433322 | xueyj | "2024-09-04T07:02:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-09-04T07:02:02Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## Uses
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## 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
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[More Information Needed]
### 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. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[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]
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- **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:**
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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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0 |
shuixingzai/xiaoshuo-fp8 | shuixingzai | "2024-09-04T07:02:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T07:02:10Z" | Entry not found |
KiraOfuru/Pony_Diffusion_v6 | KiraOfuru | "2024-09-04T07:05:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-09-04T07:03:03Z" | Model Info:
https://civitai.com/models/257749/pony-diffusion-v6-xl |
BoxMrChen/Qwen-Qwen1.5-0.5B-1725433385 | BoxMrChen | "2024-09-04T07:04:02Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-09-04T07:03:07Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[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. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
Krabat/google-gemma-7b-1725433399 | Krabat | "2024-09-04T07:03:24Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"region:us"
] | null | "2024-09-04T07:03:19Z" | ---
base_model: google/gemma-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.12.0 |
ssnsn/pretrain18 | ssnsn | "2024-09-04T07:03:52Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-09-04T07:03:27Z" | ---
license: openrail
---
|